A method, device, storage medium and electronic device for enhancing bacterial colony image

Abstract

The present invention provides a colony image enhancement method, device, storage medium and electronic device, which are applied to the field of image processing technology. The present invention obtains an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; detects the fog area and artifact area of ​​the upper light source colony image; obtains the exposure factor of the upper light source colony image based on the fog area and artifact area; obtains the lower light source colony image of the target colony based on the exposure factor; inputs the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtains the enhanced target colony image output by the image fusion model. The present invention detects and calculates the exposure factor using the fog and artifact area, and combines the upper and lower light source images in the pre-trained image fusion model for processing, thereby effectively reducing the interference of fog and artifacts in the colony image, thereby enhancing the quality of the colony image.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a colony image enhancement method, device, storage medium and electronic equipment. Background Art

[0002] In biomedical and microbiological research, accurate colony counting is a critical task that directly affects the reliability of experimental results.

[0003] Traditional colony counting methods rely on manual operations, which are not only time-consuming and labor-intensive, but also susceptible to human errors. Therefore, automated image processing techniques have been introduced to improve efficiency and accuracy. However, these technologies face significant challenges in practical applications, the most critical of which is the common interference of fog and artifacts in images.

[0004] Therefore, how to reduce the interference of fog and artifacts on colony images and improve the image quality of colony images has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of the above problems, the present invention provides a method, device, storage medium and electronic device for enhancing bacterial colony images to overcome the above problems or at least partially solve the above problems. The technical solution is as follows:

[0006] A bacterial colony image enhancement method, comprising:

[0007] Obtaining an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts;

[0008] Detecting foggy areas and artifact areas of the upper light source bacterial colony image;

[0009] Based on the fog area and the artifact area, obtaining an exposure factor of the upper light source bacterial colony image;

[0010] Based on the exposure factor, obtaining a lower light source colony image of the target colony;

[0011] The upper light source colony image and the lower light source colony image are input into a pre-trained image fusion model for image fusion, so as to obtain an enhanced target colony image output by the image fusion model.

[0012] Optionally, obtaining the exposure factor of the upper light source bacterial colony image based on the fog area and the artifact area includes:

[0013] Obtaining the area parameter and edge intensity parameter of each fog area in the upper light source colony image;

[0014] Calculate the fog factor of the upper light source colony image by using the area parameter and the edge intensity parameter of each fog area;

[0015] Obtaining brightness difference parameters and contrast difference parameters of each artifact area and its edge area in the upper light source colony image;

[0016] Calculate the artifact factor of the upper light source colony image by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area;

[0017] The exposure factor of the upper light source colony image is obtained by using the fog factor and the artifact factor.

[0018] Optionally, the calculating the fog factor of the upper light source colony image by using the area parameter and the edge intensity parameter of each fog region includes:

[0019] The area parameter and the edge strength parameter of each fog region are input into the formula:

[0020]

[0021] The fog factor of the upper light source colony image is calculated, where: is the fog factor; is the index number of the fog area; is the total number of foggy areas in the upper light source bacterial colony image; Indicates The area parameter of the fog area; Indicates The edge strength parameter of the fog area; Represents the pixel area of ​​the colony image on the light source.

[0022] Optionally, calculating the artifact factor of the upper light source colony image by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area includes:

[0023] The brightness difference parameter and the contrast difference parameter of each artifact area and its edge area are input into the formula:

[0024]

[0025] The artifact factor of the upper light source colony image is calculated, where: is the artifact factor; is the index number of the artifact area; is the total number of artifact areas in the upper light source colony image; Indicates the brightness difference parameter within the artifact area; Indicates the brightness difference parameter in the edge area of ​​the artifact area; Indicates the contrast difference parameter within the artifact area; Indicates The contrast difference parameter in the edge area of ​​the artifact area.

[0026] Optionally, obtaining the lower light source colony image of the target colony based on the exposure factor includes:

[0027] Calculate the optimal exposure time using the exposure factor and the preset basic exposure time;

[0028] The lower light source colony image of the target colony is captured according to the optimal exposure time.

[0029] Optionally, the step of inputting the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion to obtain an enhanced target colony image output by the image fusion model includes:

[0030] Extracting features of the upper light source colony image to obtain a first upper light source colony feature image;

[0031] Extracting features of the lower light source colony image to obtain a lower light source colony feature image;

[0032] Obtaining a fog attention weight and an artifact attention weight of the upper light source colony image;

[0033] Obtain a second upper light source colony feature image using the first upper light source colony feature image, the fog attention weight, and the artifact attention weight;

[0034] The second upper light source colony characteristic image is feature-fused with the lower light source colony characteristic image to obtain an enhanced target colony image of the target colony.

[0035] Optionally, the total loss function used by the image fusion model during training is the sum of an L1 loss function and a perceptual loss function.

[0036] A bacterial colony image enhancement device comprises: an upper light source bacterial colony image acquisition unit, a fog artifact area detection unit, an exposure factor acquisition unit, a lower light source bacterial colony image acquisition unit and a bacterial colony image enhancement unit.

[0037] The upper light source colony image obtaining unit is used to obtain an upper light source colony image of a target colony, wherein the upper light source colony image is a colony culture dish image with fog and artifacts;

[0038] The fog artifact area detection unit is used to detect the fog area and artifact area of ​​the upper light source bacterial colony image;

[0039] The exposure factor obtaining unit is used to obtain the exposure factor of the upper light source bacterial colony image based on the fog area and the artifact area;

[0040] The lower light source colony image obtaining unit is used to obtain the lower light source colony image of the target colony based on the exposure factor;

[0041] The colony image enhancement unit is used to input the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, so as to obtain an enhanced target colony image output by the image fusion model.

[0042] A computer-readable storage medium stores a program, wherein when the program is executed by a processor, the colony image enhancement method described in any one of the above items is implemented.

[0043] An electronic device comprising at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; and the processor is used to call program instructions in the memory to execute any of the above-mentioned colony image enhancement methods.

[0044] By means of the above technical scheme, the present invention provides a colony image enhancement method, device, storage medium and electronic device, which obtain an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; detect the fog area and artifact area of ​​the upper light source colony image; based on the fog area and artifact area, obtain the exposure factor of the upper light source colony image; based on the exposure factor, obtain the lower light source colony image of the target colony; input the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtain the enhanced target colony image output by the image fusion model. The present invention detects and calculates the exposure factor using the fog and artifact area, and combines the upper and lower light source images in the pre-trained image fusion model for processing, thereby effectively reducing the interference of fog and artifacts in the colony image, thereby enhancing the quality of the colony image.

[0045] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 A schematic diagram showing a flow chart of an implementation of a method for enhancing a bacterial colony image provided by an embodiment of the present invention;

[0048] Figure 2 A schematic flow chart of another implementation of the bacterial colony image enhancement method provided in an embodiment of the present invention is shown;

[0049] Figure 3 A schematic flow chart of another implementation of the bacterial colony image enhancement method provided in an embodiment of the present invention is shown;

[0050] Figure 4 A logic block diagram of an image fusion process of an image fusion model provided by an embodiment of the present invention is shown;

[0051] Figure 5 A logic block diagram of bacterial colony image enhancement provided by an embodiment of the present invention is shown;

[0052] Figure 6 An example schematic diagram of colony image enhancement provided by an embodiment of the present invention is shown;

[0053] Figure 7 A schematic diagram of the structure of a bacterial colony image enhancement device provided in an embodiment of the present invention is shown;

[0054] Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0056] In biomedical and microbiological research, the accuracy of colony counting is extremely important. Traditional manual methods are time-consuming and prone to errors. Therefore, automated image processing technology is introduced to improve efficiency, but it faces the challenges of fog and artifact interference.

[0057] Fog and artifacts are caused by the closed nature of the culture dish and the optical effects under single-light illumination conditions. These interference factors blur image details, affect the identification and counting of colonies, and may lead to inaccurate results in severe cases. Existing defogging and de-artifacting algorithms perform poorly when processing these single-light images and are difficult to effectively eliminate these visual interferences, mainly because they lack the ability to adapt to complex light and shadow conditions.

[0058] In addition, in order to maintain the integrity and sterility of the experiment, the culture dishes cannot be opened for counting, which further increases the difficulty of image processing. Existing solutions are difficult to achieve efficient dehazing and artifact removal without disturbing the experimental environment.

[0059] Therefore, the present invention provides a comprehensive image enhancement solution by introducing the coordinated lighting technology of upper and lower light sources. By utilizing multi-angle light sources for illumination, the influence of fog and artifacts is effectively reduced, and the contrast and detail clarity of the image are improved, thereby ensuring the accuracy and reliability of colony counting. This method not only improves the counting accuracy, but can also be used without destroying the closure of the culture dish, and has important application value and innovative significance.

[0060] The embodiment of the present invention provides a colony image enhancement method, which effectively reduces the interference of fog and artifacts in colony images through an innovative multi-step processing flow, thereby improving the image quality. First, by acquiring the upper light source image of the target colony and detecting the fog and artifact areas therein, the key factors affecting the image quality are accurately identified. Then, based on the information of these areas, the exposure factor is calculated and applied to optimize the acquisition of the lower light source colony image. This coordination strategy of upper and lower light sources ensures the capture of more comprehensive image information. Finally, the upper and lower light source images are fused using a pre-trained image fusion model to generate an enhanced target colony image. This process significantly improves the contrast and clarity of the image, reduces the interference of fog and artifacts, and thus improves the accuracy and reliability of colony counting.

[0061] like Figure 1 As shown, a schematic diagram of a process of an implementation of a method for enhancing a bacterial colony image provided by an embodiment of the present invention is provided. The method may include:

[0062] S100, obtaining an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts.

[0063] Among them, the target colonies are specific microbial communities that need to be paid attention to and counted in experiments or analyses. These colonies usually grow on culture media and form observable aggregates.

[0064] Among them, the upper light source colony image refers to the image taken after placing a light source above the culture dish where the colony is located for illumination. This lighting method can help highlight the morphology and color characteristics of the colony, and help to accurately identify and analyze the characteristics of the colony. Since the colony is photographed in the culture dish, it is easily affected by factors including light intensity, light angle, uneven lighting, culture dish material, high humidity and temperature changes, resulting in problems such as fog and artifacts in the upper light source colony image.

[0065] Specifically, the embodiments of the present invention can obtain the original upper light source colony image by setting a light source above the colony for shooting.

[0066] S110, detecting fog areas and artifact areas of the upper light source bacterial colony image.

[0067] The foggy area refers to the blurred or unclear area in the colony image due to light scattering or humidity. The foggy area usually reduces the contrast of the image, making it difficult to identify the details of the colony.

[0068] The artifact area refers to the non-real information or distorted part in the colony image caused by light reflection, equipment failure or other factors. These artifacts may appear as unnatural bright spots, shadows or stripes, interfering with the accurate identification and analysis of colonies.

[0069] Specifically, the embodiment of the present invention can detect and segment the foggy area and artifact area in the upper light source colony image by using the YOLO (You Only Look Once) algorithm. The YOLO algorithm is an advanced technology for target detection. Its core concept is to simplify the entire process by converting the detection problem into a regression problem. The embodiment of the present invention can effectively detect and segment the foggy area and artifact area in the upper light source colony image by using the YOLO algorithm, quickly identify these influencing factors, and thus improve the accuracy and reliability of image analysis.

[0070] The embodiment of the present invention can input the upper light source colony image into a pre-trained fog segmentation model to obtain multiple fog areas detected by the fog segmentation model in the upper light source colony image, wherein the fog segmentation model is a target detection network model based on the YOLO algorithm, which is used to detect fog areas.

[0071] An embodiment of the present invention can input the upper light source colony image into a pre-trained artifact segmentation model to obtain multiple artifact regions detected by the artifact segmentation model in the upper light source colony image, wherein the artifact segmentation model is a target detection network model based on the YOLO algorithm, which is used to detect artifact regions.

[0072] S120, obtaining an exposure factor of the upper light source colony image based on the fog area and the artifact area.

[0073] Among them, the exposure factor is a parameter calculated by analyzing the fog area and artifact area in the colony image of the upper light source. It is used to ensure that the captured colony image reaches the optimal exposure level after minimizing these interference factors (fog and artifacts), thereby improving the accuracy and reliability of image analysis.

[0074] The embodiment of the present invention obtains the exposure factor of the upper light source colony image so as to better control the brightness and contrast of the image when subsequently acquiring the lower light source colony image, thereby making the features of the lower light source colony image more obvious and recognizable.

[0075] S130, obtaining a lower light source colony image of the target colony based on the exposure factor.

[0076] Among them, the bottom light colony image refers to the colony image taken under specific lighting conditions, in which the light source is located below the target colony. This arrangement is often used to observe and analyze transparent or translucent colony samples, such as bacterial colonies in a petri dish. This lighting method helps to improve the contrast and detail visibility of colony samples because the light passes through the sample from below, making the structure, shape, and edges of the colony more clearly visible.

[0077] The embodiment of the present invention can adjust the setting of the lower light source camera through the calculated exposure factor to obtain the optimal exposure time. The target bacterial colony is photographed using the optimal exposure time to obtain a clear and accurate lower light source bacterial colony image.

[0078] The embodiment of the present invention uses a lower light source to capture colony images, which can better observe the internal features and boundaries of the colonies, which is very important for applications such as microbiological research, quality control, and automated colony counting. Through a reliable exposure factor, the image quality under such lighting conditions can be optimized to ensure that the information obtained is accurate and has good repeatability.

[0079] Since the upper and lower light sources capture images in the same setting or environment, they may be affected by similar environmental factors, such as lighting conditions, colony characteristics, etc. Therefore, by detecting quality issues (such as fog and artifacts) in the colony images of the upper light source, it is possible to infer and predict how these issues may appear in the colony images of the lower light source. At the same time, the fog and artifacts detected in the colony images of the upper light source provide useful information about light scattering, reflection, and light inhomogeneity. These effects can be quantified by calculating the exposure factor. This information can be used to adjust the exposure time of the lower light source to compensate for or reduce these effects. In addition, after detecting problems in the colony images of the upper light source, the exposure time of the lower light source is dynamically adjusted to ensure the best quality of colony images taken under the lower light source condition. This dynamic adjustment can quickly adapt based on the problems of the colony images of the upper light source detected in real time so that consistent image quality can be maintained under different shooting conditions.

[0080] S140, inputting the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtaining an enhanced target colony image output by the image fusion model.

[0081] The image fusion model can be a model based on a deep convolutional neural network, which is used to extract the image features of the upper light source colony image and the lower light source colony image, extract and combine the important information in these images layer by layer, and finally generate a fused target colony image through the output layer of the network. Since the details and features of the input image are integrated, the visualization effect and information content of the target colony image are richer, which is helpful for the accuracy and reliability of subsequent colony counting.

[0082] The embodiment of the present invention inputs the upper light source colony image and the lower light source colony image into a pre-trained image fusion model, and uses intelligent fusion technology to process the interference of fog and artifacts through feature extraction and Attention modules to generate an enhanced target colony image.

[0083] The present invention provides a colony image enhancement method, which includes: obtaining an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; detecting the fog area and the artifact area of ​​the upper light source colony image; obtaining the exposure factor of the upper light source colony image based on the fog area and the artifact area; obtaining the lower light source colony image of the target colony based on the exposure factor; inputting the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtaining an enhanced target colony image output by the image fusion model. The present invention detects and calculates the exposure factor using the fog and artifact area, and combines the upper and lower light source images in the pre-trained image fusion model for processing, thereby effectively reducing the interference of fog and artifacts in the colony image, thereby enhancing the quality of the colony image.

[0084] Optional, based on Figure 1 The method shown, such as Figure 2 As shown, a flow chart of another implementation of the bacterial colony image enhancement method provided in an embodiment of the present invention is shown, and step S120 may include:

[0085] S200, obtaining area parameters and edge intensity parameters of each fog region in the upper light source colony image.

[0086] The area parameter refers to the size of the fog region in the colony image of the upper light source, which is determined by the number of pixels in the fog region.

[0087] Among them, the edge strength parameter is used to measure the sharpness or contrast of the regional boundary of the fog area in the upper light source colony image, reflecting the degree of difference between the fog area and its background or adjacent area.

[0088] The embodiment of the present invention may use gradient calculation to identify places with large intensity changes (ie, edges) in the fog region, and then calculate edge intensity parameters.

[0089] Specifically, the embodiment of the present invention can use the edge detection operator to calculate the gradient of the fog area in the horizontal and vertical directions through the convolution operation. The horizontal gradient represents the intensity change of the image in the horizontal direction, and the vertical gradient represents the change in the vertical direction. Then, the gradient amplitude of each pixel is calculated by combining the horizontal gradient and the vertical gradient. Finally, the calculated gradient amplitude is used to represent the edge intensity of each position in the image.

[0090] When the edge detection operator is the Canny operator, the fog area The calculation process of the edge strength parameter is as follows:

[0091] First calculate the horizontal gradient of the fog area and vertical gradient :

[0092]

[0093]

[0094] Combined with the horizontal gradient and vertical gradient, the edge strength parameter is calculated:

[0095]

[0096] The embodiment of the present invention uses the Canny operator to calculate the edge strength of the fog area, which can effectively reduce noise interference and accurately and continuously highlight the area boundary, thereby providing clear contour information to support further analysis.

[0097] S210, using the area parameter and edge intensity parameter of each fog area, calculate the fog factor of the upper light source colony image.

[0098] Among them, the fog factor is a comprehensive parameter used to quantify the characteristics of foggy areas in the analysis of colony images of upper light sources. It combines the area and edge strength of each foggy area to provide information about the overall "foggy" degree of the area. The fog factor can effectively capture the extension degree and edge blur of the foggy area, thereby helping to identify and distinguish different foggy features in image analysis.

[0099] Optional, in the above Figure 2 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, the fog factor of the upper light source colony image is calculated using the area parameter and edge intensity parameter of each fog area, which may specifically include:

[0100] Input the area parameter and edge strength parameter of each fog region into the formula:

[0101]

[0102] Calculate the fog factor of the colony image of the upper light source, where is the fog factor; is the index number of the fog area; is the total number of foggy areas in the colony image of the upper light source; Indicates Area parameter of the fog area; Indicates Edge strength parameter of the fog area; Represents the pixel area of ​​the colony image on the light source.

[0103] The embodiment of the present invention combines the area parameter and edge strength parameter of each fog area into one formula to calculate the fog factor, so as to accurately quantify the fog characteristics in the bacterial colony image of the upper light source and provide a reliable basis for the subsequent calculation of the exposure factor.

[0104] S220, obtaining brightness difference parameters and contrast difference parameters of each artifact area and its edge area in the upper light source colony image.

[0105] The brightness difference parameter is the difference between the average brightness of the artifact area and its edge area, that is: ,in, is the brightness difference parameter, is the average brightness of the artifact area, is the average brightness of the edge area of ​​the artifact area, Indicates absolute value.

[0106] Among them, the contrast difference parameter is the difference between the average contrast of the artifact area and its edge area, that is, ,in, is the contrast difference parameter, is the average contrast of the artifact area, is the average contrast of the edge area of ​​the artifact area, Indicates absolute value.

[0107] S230, calculating the artifact factor of the upper light source colony image by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area.

[0108] Among them, the artifact factor is a comprehensive parameter used to quantify and characterize the influence of the artifact area in the upper light source colony image. By using the brightness difference parameter and contrast difference parameter of each artifact area and its edge area, the artifact factor is calculated to obtain the relative significance and influence of these artifacts in the entire upper light source colony image.

[0109] Optional, in the above Figure 2 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, the artifact factor of the upper light source colony image is calculated by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area, which may specifically include:

[0110] Input the brightness difference parameter and contrast difference parameter of each artifact area and its edge area into the formula:

[0111]

[0112] Calculate the artifact factor of the colony image of the upper light source, where: is the artifact factor; is the index number of the artifact area; is the total number of artifact areas in the colony image of the upper light source; Indicates Brightness difference parameters within the artifact area; Indicates Brightness difference parameter within the edge area of ​​the artifact area; Indicates Contrast difference parameter within the artifact area; Indicates The contrast difference parameter within the edge area of ​​the artifact area.

[0113] The embodiment of the present invention can effectively quantify the influence of artifacts in the colony image of the upper light source by normalizing the brightness difference and contrast difference parameters of each artifact area and its edge area and then averaging them, thereby providing a reliable basis for the subsequent calculation of the exposure factor.

[0114] S240, using the fog factor and the artifact factor, obtaining the exposure factor of the upper light source colony image.

[0115] Specifically, the embodiment of the present invention may input the fog factor and the artifact factor into the formula:

[0116]

[0117] Calculate the exposure factor of the colony image of the upper light source, where: is the exposure factor; is the fog factor, is the artifact factor.

[0118] The embodiment of the present invention integrates the fog factor and the artifact factor to calculate the exposure factor, which can comprehensively evaluate the overall quality of the upper light source colony image and the degree to which it is affected by the environment and artifacts, thereby facilitating the subsequent adjustment of the exposure time of the lower light source colony image.

[0119] The embodiment of the present invention calculates the corresponding fog factor and artifact factor by accurately measuring the area and edge intensity of each fog region in the colony image of the upper light source, as well as the brightness and contrast differences between the artifact region and its edge, and finally forms a comprehensive exposure factor, which helps to adjust the exposure time of the lower light source, reduce the interference of fog and artifacts on the colony image of the lower light source, and improve the quality and clarity of the colony image of the lower light source.

[0120] Optional, in the above Figure 1 On the basis of one or more corresponding embodiments, in another optional embodiment provided by the embodiment of the present invention, obtaining the colony image of the target colony under the light source based on the exposure factor may specifically include:

[0121] The optimal exposure time is calculated using the exposure factor and the preset basic exposure time. The colony image of the target colony under the light source is taken according to the optimal exposure time.

[0122] The preset basic exposure time is an initially set exposure time used as a benchmark for capturing images. The preset basic exposure time can be set based on standard lighting conditions and average colony sample characteristics, or can be set based on experience, average values, or standard experimental conditions to ensure that colony images of acceptable quality can be generated in most situations.

[0123] The optimal exposure time is the exposure time that is dynamically adjusted after considering specific environmental factors and image quality requirements. The optimal exposure time is based on the actual detected fog and artifacts, and is corrected by the calculated exposure factor. The optimal exposure time is designed to optimize the clarity and contrast of the image so that the final image of the colony under the light source can achieve the best quality under specific conditions. The dynamic adjustment of the optimal exposure time can ensure more accurate and consistent imaging effects in response to changes in different shooting conditions.

[0124] The embodiment of the present invention can calculate the optimal exposure time by applying the calculated exposure factor to the setting of the lower light source camera, starting from the preset basic exposure time (e.g., 7000 microseconds). Then, the lower light source colony image of the target colony is captured using this optimal exposure time to ensure the best image quality.

[0125] Specifically, the embodiment of the present invention may input the exposure factor and the preset basic exposure time into the formula:

[0126]

[0127] Calculate the optimal exposure time, where is the optimal exposure time, is the exposure factor, The preset basic exposure time.

[0128] The embodiment of the present invention combines the preset basic exposure time with the exposure factor to dynamically calculate the optimal exposure time to cope with environmental changes and the diversity of sample characteristics, thereby improving the quality of the colony image under the light source.

[0129] Optional, based on Figure 1 The method shown, such as Figure 3 As shown, a flow chart of another implementation of the bacterial colony image enhancement method provided in an embodiment of the present invention is shown, and step S140 may include:

[0130] S300 , extracting features from the upper light source colony image to obtain a first upper light source colony feature image.

[0131] Specifically, the embodiment of the present invention can use the ResNet18 convolutional neural network in the image fusion model to extract features of the upper light source colony image to obtain the first upper light source colony feature image, that is:

[0132]

[0133] in, is the characteristic image of the colony under the first upper light source, This is the image of the colony on the light source.

[0134] S310, extract features from the lower light source colony image to obtain a lower light source colony feature image.

[0135] Specifically, the embodiment of the present invention can use the ResNet18 convolutional neural network in the image fusion model to extract features of the lower light source colony image to obtain the lower light source colony feature image, that is:

[0136]

[0137] in, is the characteristic image of the colony under the lower light source, This is the colony image under the light source.

[0138] S320, obtaining the fog attention weight and artifact attention weight of the upper light source colony image.

[0139] Specifically, the embodiment of the present invention can use the sigmoid function provided by the Attention module in the image fusion model to convert the fog mask area and the artifact mask area into the fog attention weight and the artifact attention weight, respectively, that is:

[0140]

[0141]

[0142] in, is the fog mask area, is the artifact mask area, is the fog attention weight, is the artifact attention weight.

[0143] S330. Obtain a second upper light source colony feature image using the first upper light source colony feature image, fog attention weight, and artifact attention weight.

[0144] Specifically, the embodiment of the present invention can apply the fog attention weight and the artifact attention weight to the first upper light source colony feature image, adjust the intensity of each pixel or feature in the first upper light source colony feature image according to the fog attention weight and the artifact attention weight, and obtain the second upper light source colony feature image, that is:

[0145]

[0146] in, is the characteristic image of the colony under the second upper light source, is the characteristic image of the colony under the first upper light source, is the fog attention weight, is the artifact attention weight.

[0147] S340, performing feature fusion on the second upper light source colony feature image and the lower light source colony feature image to obtain an enhanced target colony image of the target colony.

[0148] Specifically, the embodiment of the present invention can use feature connection to fuse the second upper light source colony feature image with the lower light source colony feature image, and use convolution to perform dimensionality reduction processing, and finally convert the fused feature map into a target colony image through upsampling, thereby enhancing the colony image, that is:

[0149]

[0150]

[0151] in, is the fusion feature map, represents the convolution operation, represents the feature connection operation, is the characteristic image of the colony under the second upper light source, is the characteristic image of the colony under the lower light source, is the target colony image, Represents an upsampling operation.

[0152] The embodiment of the present invention can capture the unique information and details under each light source separately by extracting features from the colony images of the upper light source and the lower light source. Furthermore, by calculating the attention weights of the fog and artifacts of the upper light source image, these factors that may affect the image quality can be adjusted and compensated more accurately, thereby generating a clearer second upper light source colony feature image. Subsequently, this feature image is feature fused with the lower light source feature image, making full use of the complementary information under different light sources. Ultimately, this comprehensive processing method of multiple light sources and multiple features can generate an enhanced target colony image with higher quality and richer details, which helps to improve the analysis accuracy and reliability of the colony image.

[0153] Optionally, the total loss function used by the image fusion model during training is the sum of the L1 loss function and the perceptual loss function, that is:

[0154]

[0155] in, is the total loss function, is the L1 loss function, is the perceptual loss function.

[0156] The expression of L1 loss function is:

[0157]

[0158] in, is the L1 loss function, is the total number of pixels involved in the calculation, Indicates the pixel number, The fused image, Represents the target image.

[0159] The expression of the perceptual loss function is:

[0160]

[0161] in, is the perceptual loss function, is the total number of feature channels, is the characteristic channel number, Indicates The feature map output by the feature channels is is the fused image, is the target image.

[0162] By combining the L1 loss function with the perceptual loss function, the image fusion model in the training process can simultaneously optimize the pixel-level accuracy and visual perception quality, thereby generating a more accurate and visually satisfactory fused image.

[0163] In order to facilitate understanding of the overall process of the colony image enhancement method provided by the embodiment of the present invention, it is explained here by way of example: In the data set preparation stage: the embodiment of the present invention creates three sets of data sets: a fog data set, an artifact data set, and an image fusion model data set, which are respectively used to train the fog segmentation model and the artifact segmentation model based on the YOLO algorithm, wherein the fog data set contains upper light source colony images taken under different conditions and marked with fog areas. The artifact data set includes the artifact areas marked in the upper light source colony images. The image fusion model data set contains the upper light source colony images, masks of fog and artifacts, and an ideal fused image. The embodiment of the present invention can standardize the images in the data set to improve the model training effect.

[0164] In the YOLO model training stage: the embodiment of the present invention can use the target detection network such as YOLOv5 as the model architecture, and use the fog and artifact data sets to train the segmentation model respectively. The training process includes: 1. Data loading and enhancement: including rotation, cropping, brightness adjustment, etc. 2. Adjust the loss function and hyperparameters to optimize the model performance. 3. Model verification and performance evaluation to ensure effective identification of fog and artifacts.

[0165] During the image fusion model training phase: Figure 4The figure shows a logic block diagram of the image fusion process of the image fusion model provided by an embodiment of the present invention. In the embodiment of the present invention, ResNet18 can be used as a feature extraction module, combined with the Attention module to process fog and artifact masks, and fused with the image to output an enhanced image. Next, the image fusion model dataset is used for model training. The training process includes: 1. Data loading and enhancement: including rotation, cropping, brightness adjustment, etc. 2. Adjust the loss function and hyperparameters to optimize model performance. 3. Model verification and performance evaluation to ensure the output of high-quality fused images.

[0166] In the image processing stage: Figure 5 FIG. 1 is a logic block diagram of colony image enhancement provided by an embodiment of the present invention. Figure 6 The example schematic diagram of colony image enhancement provided by an embodiment of the present invention is shown. First, a high-resolution camera is used to shoot under uniform light to avoid reflections, and an upper light source colony image is obtained. Secondly, the upper light source colony image is detected using a fog segmentation model, all fog areas are extracted, their areas and edge strengths are calculated, and a binary image of the fog area is obtained using a binarization method. At the same time, the upper light source image is processed using an artifact segmentation model, all artifact areas are identified, and brightness differences and contrast differences are calculated. Next, the quantitative parameters of all fog and artifact areas are calculated to obtain fog factors and artifact factors, respectively, and then the exposure factor is weighted and calculated, and the calculated exposure factor is multiplied by the basic exposure time to obtain the optimal exposure time of the lower light source. Using the calculated optimal exposure time, the lower light source camera is controlled to capture the lower light source colony image. Finally, the upper light source colony image and the lower light source colony image, as well as the fog and artifact area data, are input into the image fusion model to generate an enhanced colony image, and the final enhanced colony image is saved or displayed for subsequent colony counting and analysis.

[0167] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0168] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0169] Corresponding to the above method embodiment, the present invention also provides a bacterial colony image enhancement device, the structure of which is as follows: Figure 7 As shown, it may include: an upper light source colony image obtaining unit 10, a fog artifact area detection unit 20, an exposure factor obtaining unit 30, a lower light source colony image obtaining unit 40 and a colony image enhancing unit 50.

[0170] The upper light source colony image obtaining unit 10 is used to obtain an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts.

[0171] The fog artifact area detection unit 20 is used to detect the fog area and artifact area of ​​the upper light source bacterial colony image.

[0172] The exposure factor obtaining unit 30 is used to obtain the exposure factor of the upper light source bacterial colony image based on the fog area and the artifact area.

[0173] The lower light source colony image obtaining unit 40 is used to obtain the lower light source colony image of the target colony based on the exposure factor.

[0174] The colony image enhancement unit 50 is used to input the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtain an enhanced target colony image output by the image fusion model.

[0175] Optionally, the exposure factor obtaining unit 30 includes: a fog parameter obtaining subunit, a fog factor calculating subunit, an artifact parameter obtaining subunit, an artifact factor calculating subunit and an exposure factor obtaining subunit.

[0176] A fog parameter acquisition subunit is used to obtain the area parameter and edge intensity parameter of each fog area in the upper light source colony image;

[0177] The fog factor calculation subunit is used to calculate the fog factor of the upper light source colony image by using the area parameter and edge strength parameter of each fog area;

[0178] An artifact parameter acquisition subunit is used to obtain brightness difference parameters and contrast difference parameters of each artifact area and its edge area in the upper light source colony image;

[0179] The artifact factor calculation subunit is used to calculate the artifact factor of the upper light source colony image by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area;

[0180] The exposure factor obtaining subunit is used to obtain the exposure factor of the upper light source colony image by using the fog factor and the artifact factor.

[0181] Optionally, the fog factor calculation subunit may be used to input the area parameter and edge strength parameter of each fog region into the formula:

[0182]

[0183] Calculate the fog factor of the colony image of the upper light source, where is the fog factor; is the index number of the fog area; is the total number of foggy areas in the colony image of the upper light source; Indicates Area parameter of the fog area; Indicates Edge strength parameter of the fog area; Represents the pixel area of ​​the colony image on the light source.

[0184] Optionally, the artifact factor calculation subunit may be used to input the brightness difference parameter and the contrast difference parameter of each artifact region and its edge region into the formula:

[0185]

[0186] Calculate the artifact factor of the colony image of the upper light source, where: is the artifact factor; is the index number of the artifact area; is the total number of artifact areas in the colony image of the upper light source; Indicates Brightness difference parameters within the artifact area; Indicates Brightness difference parameter within the edge area of ​​the artifact area; Indicates Contrast difference parameter within the artifact area; Indicates The contrast difference parameter within the edge area of ​​the artifact area.

[0187] Optionally, the lower light source colony image obtaining unit 40 can be specifically used to calculate the optimal exposure time using the exposure factor and the preset basic exposure time; and capture the lower light source colony image of the target colony according to the optimal exposure time.

[0188] Optionally, the colony image enhancement unit 50 may include: a first upper light source colony feature image obtaining subunit, a lower light source colony feature image obtaining subunit, an attention weight obtaining subunit, a second upper light source colony feature image obtaining subunit and a target colony image or subunit.

[0189] The first upper light source colony characteristic image obtaining subunit is used to extract characteristics of the upper light source colony image to obtain the first upper light source colony characteristic image.

[0190] The lower light source colony characteristic image acquisition subunit is used to extract the characteristics of the lower light source colony image to obtain the lower light source colony characteristic image.

[0191] The attention weight acquisition subunit is used to obtain the fog attention weight and artifact attention weight of the upper light source colony image.

[0192] The second upper light source colony characteristic image obtaining subunit is used to obtain the second upper light source colony characteristic image using the first upper light source colony characteristic image, fog attention weight and artifact attention weight.

[0193] The target colony image or subunit is used to perform feature fusion on the second upper light source colony feature image and the lower light source colony feature image to obtain an enhanced target colony image of the target colony.

[0194] Optionally, the total loss function used by the image fusion model during training is the sum of the L1 loss function and the perceptual loss function.

[0195] The present invention provides a colony image enhancement device, which is used to: obtain an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; detect the fog area and artifact area of ​​the upper light source colony image; obtain the exposure factor of the upper light source colony image based on the fog area and artifact area; obtain the lower light source colony image of the target colony based on the exposure factor; input the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtain the enhanced target colony image output by the image fusion model. The present invention detects and calculates the exposure factor using the fog and artifact area, and combines the upper and lower light source images in the pre-trained image fusion model for processing, thereby effectively reducing the interference of fog and artifacts in the colony image, thereby enhancing the quality of the colony image.

[0196] Regarding the device in the above embodiment, the specific manner in which each unit performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0197] The colony image enhancement device includes a processor and a memory. The upper light source colony image acquisition unit 10, the fog artifact area detection unit 20, the exposure factor acquisition unit 30, the lower light source colony image acquisition unit 40 and the colony image enhancement unit 50 are all stored in the memory as program units, and the processor executes the above program units stored in the memory to realize the corresponding functions.

[0198] The processor contains a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters are adjusted to detect and use the fog and artifact areas to calculate the exposure factor, and the upper and lower light source images are combined in the pre-trained image fusion model to effectively reduce the fog and artifact interference in the colony image, thereby enhancing the quality of the colony image.

[0199] An embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the colony image enhancement method is implemented.

[0200] An embodiment of the present invention provides a processor, which is used to run a program, wherein the colony image enhancement method is executed when the program is running.

[0201] like Figure 8 As shown, an embodiment of the present invention provides an electronic device 1000, which includes at least one processor 1001, at least one memory 1002 connected to the processor 1001, and a bus 1003; wherein the processor 1001 and the memory 1002 communicate with each other through the bus 1003; the processor 1001 is used to call the program instructions in the memory 1002 to execute the above-mentioned bacterial colony image enhancement method. The electronic device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0202] The present invention also provides a computer program product, which, when executed on an electronic device, is suitable for executing a program that initializes the steps of the bacterial colony image enhancement method.

[0203] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatuses, electronic devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable device to generate a machine, so that the instructions executed by the processor of the computer or other programmable device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0204] In a typical configuration, an electronic device includes one or more processors (CPU), a memory, and a bus. The electronic device may also include an input / output interface, a network interface, and the like.

[0205] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

[0206] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0207] In the description of the present invention, it should be understood that the terms "up", "down", "front", "back", "left" and "right" etc. indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the positions or elements referred to must have specific directions, be constructed and operate in specific directions. Therefore, they should not be understood as limitations of the present invention.

[0208] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0209] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0210] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for enhancing a bacterial colony image, characterized in that: include: Obtaining an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; Detecting foggy areas and artifact areas of the upper light source bacterial colony image; Based on the fog area and the artifact area, obtaining an exposure factor of the upper light source bacterial colony image; Based on the exposure factor, obtaining a lower light source colony image of the target colony; Inputting the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, and obtaining an enhanced target colony image output by the image fusion model; Wherein, obtaining the exposure factor of the upper light source bacterial colony image based on the fog area and the artifact area includes: Obtaining a region area parameter and an edge strength parameter of each of the fog regions in the upper light source colony image, wherein the edge strength parameter is used to measure the sharpness or contrast of the region boundary of the fog region in the upper light source colony image; Calculate the fog factor of the upper light source colony image by using the area parameter and the edge intensity parameter of each fog region, wherein the fog factor is a comprehensive parameter used to quantify the characteristics of the fog region in the upper light source colony image analysis; Obtaining a brightness difference parameter and a contrast difference parameter of each of the artifact regions and their edge regions in the upper light source colony image, wherein the brightness difference parameter is the difference between the average brightness of the artifact region and its edge region, and the contrast difference parameter is the difference between the average contrast of the artifact region and its edge region; Using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area, the artifact factor of the upper light source colony image is calculated, wherein the artifact factor is a comprehensive parameter used to quantify and characterize the influence of the artifact area in the upper light source colony image; The exposure factor of the upper light source colony image is obtained by using the fog factor and the artifact factor.

2. The method according to claim 1, characterized in that The step of calculating the fog factor of the upper light source colony image by using the area parameter and the edge intensity parameter of each fog area includes: The area parameter and the edge strength parameter of each fog region are input into the formula: ; The fog factor of the upper light source colony image is calculated, where: is the fog factor; is the index number of the fog area; is the total number of foggy areas in the upper light source bacterial colony image; Indicates The area parameter of the fog area; Indicates The edge strength parameter of the fog area; Represents the pixel area of ​​the colony image on the light source.

3. The method according to claim 1, characterized in that The step of calculating the artifact factor of the upper light source colony image by using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area includes: The brightness difference parameter and the contrast difference parameter of each artifact area and its edge area are input into the formula: ; The artifact factor of the upper light source colony image is calculated, where: is the artifact factor; is the index number of the artifact area; is the total number of artifact areas in the upper light source colony image; Indicates the brightness difference parameter of the artifact area and its edge area; Indicates The average brightness within the edge area of ​​the artifact area; Indicates the contrast difference parameter of the artifact area and its edge area; Indicates The average contrast in the edge area of ​​the artifact area.

4. The method according to claim 1, characterized in that The step of obtaining a lower light source colony image of the target colony based on the exposure factor includes: Calculate the optimal exposure time using the exposure factor and the preset basic exposure time; The lower light source colony image of the target colony is captured according to the optimal exposure time.

5. The method according to claim 1, characterized in that The step of inputting the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion to obtain an enhanced target colony image output by the image fusion model includes: Extracting features of the upper light source colony image to obtain a first upper light source colony feature image; Extracting features of the lower light source colony image to obtain a lower light source colony feature image; Obtaining a fog attention weight and an artifact attention weight of the upper light source colony image; Obtain a second upper light source colony feature image using the first upper light source colony feature image, the fog attention weight, and the artifact attention weight; The second upper light source colony characteristic image is feature-fused with the lower light source colony characteristic image to obtain an enhanced target colony image of the target colony.

6. The method according to any one of claims 1 to 5, characterized in that The total loss function used by the image fusion model during training is the sum of the L1 loss function and the perceptual loss function.

7. A bacterial colony image enhancement device, characterized in that: include: Upper light source colony image acquisition unit, fog artifact area detection unit, exposure factor acquisition unit, lower light source colony image acquisition unit and colony image enhancement unit, The upper light source colony image obtaining unit is used to obtain an upper light source colony image of a target colony, wherein the upper light source colony image is an image of a colony culture dish with fog and artifacts; The fog artifact area detection unit is used to detect the fog area and artifact area of ​​the upper light source bacterial colony image; The exposure factor obtaining unit is used to obtain the exposure factor of the upper light source bacterial colony image based on the fog area and the artifact area; The lower light source colony image obtaining unit is used to obtain the lower light source colony image of the target colony based on the exposure factor; The colony image enhancement unit is used to input the upper light source colony image and the lower light source colony image into a pre-trained image fusion model for image fusion, so as to obtain an enhanced target colony image output by the image fusion model; Wherein, the exposure factor obtaining unit is specifically used for: Obtaining a region area parameter and an edge strength parameter of each of the fog regions in the upper light source colony image, wherein the edge strength parameter is used to measure the sharpness or contrast of the region boundary of the fog region in the upper light source colony image; Calculate the fog factor of the upper light source colony image by using the area parameter and the edge intensity parameter of each fog region, wherein the fog factor is a comprehensive parameter used to quantify the characteristics of the fog region in the upper light source colony image analysis; Obtaining a brightness difference parameter and a contrast difference parameter of each of the artifact regions and their edge regions in the upper light source colony image, wherein the brightness difference parameter is the difference between the average brightness of the artifact region and its edge region, and the contrast difference parameter is the difference between the average contrast of the artifact region and its edge region; Using the brightness difference parameter and the contrast difference parameter of each artifact area and its edge area, the artifact factor of the upper light source colony image is calculated, wherein the artifact factor is a comprehensive parameter used to quantify and characterize the influence of the artifact area in the upper light source colony image; The exposure factor of the upper light source colony image is obtained by using the fog factor and the artifact factor.

8. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the colony image enhancement method according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: The electronic device includes at least one processor, and at least one memory and a bus connected to the processor; wherein the processor and the memory communicate with each other via the bus; and the processor is used to call program instructions in the memory to execute the colony image enhancement method as described in any one of claims 1 to 6.

Images