An image processing method, apparatus, device and storage medium

Abstract

The application discloses an image processing method and device, equipment and storage medium, and relates to the technical field of display control. The method comprises the following steps: acquiring an input original image signal through a first preset interface, and decomposing the original image signal to separate channels in a target color space; dividing the separate channels into a first channel and a second channel, and performing blur processing on the first channel to obtain processed first channel data; merging the processed first channel data and second channel data of the second channel to obtain a to-be-processed image signal, compensating for brightness loss of the to-be-processed image signal through a gamma correction technology to obtain a target image signal, and displaying the target image signal through a target display. The application can make part of the light in the displayed pattern not participate in imaging on the retina of the user's eyes, so that the proportion of ambient light entering the user's eyes is increased, and the problems of visual fatigue and myopia deepening are improved.

Description

Technical Field

[0001] This invention relates to the field of display control technology, and in particular to an image processing method, apparatus, device, and storage medium. Background Technology

[0002] In the field of display control technology, myopia prevention has become an important direction in the research and development of electronic display devices. Traditional myopia prevention displays mostly employ brightness adjustment, color temperature adjustment, or blue light filtering technologies, but these methods fail to optimize for the light intensity distribution in retinal imaging. Some research attempts to modify the screen's light-emitting characteristics through optical films, but such solutions have significant drawbacks: on the one hand, hardware modifications are costly and difficult to adapt to mainstream display devices; on the other hand, the films can lead to a decrease in display brightness. Furthermore, existing color processing technologies, such as RGB channel separation, are mainly applied to image enhancement and color reproduction, and have not yet been integrated with the needs of visual health protection.

[0003] In conclusion, how to use image processing technology to improve visual fatigue and myopia progression is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide an image processing method, apparatus, device, and storage medium that can increase the proportion of non-image-forming light, i.e., ambient light, in the displayed image while ensuring clear visual recognition of the displayed content, thereby alleviating eye strain and myopia progression. The specific solution is as follows:

[0005] In a first aspect, this application provides an image processing method, comprising:

[0006] The input raw image signal is obtained through the first preset interface, and the raw image signal is decomposed to decompose the raw image signal into each independent channel in the target color space.

[0007] Each independent channel is divided into a first channel and a second channel, and the first channel is blurred based on the target blurring parameters to obtain the processed first channel data;

[0008] The processed first channel data and the second channel data corresponding to the second channel are merged to obtain the image signal to be processed. The brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, and the target image signal is displayed through the target display.

[0009] Optionally, the step of decomposing the original image signal into independent channels in the target color space includes:

[0010] The original image signal is decomposed into independent channels in the RGB color space;

[0011] Alternatively, the original image signal can be decomposed into independent channels in the CMYK color space.

[0012] Optionally, before performing blurring processing on the first channel based on the target blurring parameters to obtain the processed first channel data, the method further includes:

[0013] Current ambient light data is acquired using an ambient light sensor; the ambient light data includes light intensity and spectral characteristics.

[0014] The target distance between the user's eyes and the target display is determined using a distance sensor;

[0015] The blur radius is determined based on the ambient light data and the target distance using a preset algorithm, and the target blur parameters are determined based on the blur radius.

[0016] Optionally, the step of blurring the first channel based on the target blurring parameters to obtain the processed first channel data includes:

[0017] Gaussian blurring is performed on the first channel based on the two-dimensional Gaussian function and the target blurring parameters to obtain the processed first channel data;

[0018] Alternatively, the first channel can be low-pass filtered based on a preset convolution kernel and the target blurring parameters to obtain the processed first channel data.

[0019] Optionally, dividing each of the independent channels into a first channel and a second channel includes:

[0020] If the independent channel is an independent channel in the RGB color space, then the independent channel is the blue channel, the red channel, and the green channel;

[0021] Based on the characteristics of human vision, the blue channel in the independent channel is identified as a short-wavelength light channel, and the red and green channels in the independent channel are identified as non-short-wavelength light channels;

[0022] The shortwavelength optical channel is designated as the first channel, and the non-shortwavelength optical channel is designated as the second channel.

[0023] Optionally, dividing each of the independent channels into a first channel and a second channel includes:

[0024] If the independent channel is an independent channel in the RGB color space, then the display ratio of each color in the original image signal is determined, and the color with the largest display ratio is determined as the target reference color;

[0025] Determine the brightness of each independent channel corresponding to the target reference color, and determine the independent channel with the highest brightness as the target channel;

[0026] If the target channel is not the blue channel, then the target channel and the blue channel are determined as the first channel, and the remaining independent channels in the RGB color space other than the first channel are determined as the second channel. A first blur radius corresponding to the blue channel and a second blur radius corresponding to the target channel are set so that the blue channel is blurred based on the first blur radius and the target channel is blurred based on the second blur radius; the first blur radius is greater than the second blur radius.

[0027] If the target channel is the blue channel, then the blue channel is directly determined as the first channel, the red and green channels in the RGB color space are determined as the second channel, and the third blur radius corresponding to the blue channel is determined so that the blue channel can be blurred based on the third blur radius.

[0028] Optionally, the step of compensating for the brightness loss of the image signal to be processed using gamma correction technology to obtain the target image signal, and displaying the target image signal through a target display, includes:

[0029] The target compensation coefficient is determined based on the ambient light data collected by the ambient light sensor, and the brightness loss corresponding to the image signal to be processed is determined based on the target compensation coefficient and the processed first channel data.

[0030] Based on the brightness loss, a target compensation value is determined, and the target image signal is obtained by compensating the image signal to be processed based on the target compensation value using the gamma correction algorithm.

[0031] The target image signal is converted into a corresponding analog voltage, and the analog voltage is transmitted to the target display through a second preset interface, and the target image signal is displayed through the target display.

[0032] Secondly, this application provides an image processing apparatus, comprising:

[0033] The image signal decomposition module is used to acquire the input raw image signal through a first preset interface and decompose the raw image signal to decompose the raw image signal into independent channels in the target color space.

[0034] The channel fuzzing module is used to divide each of the independent channels into a first channel and a second channel, and to perform fuzzing processing on the first channel based on the target fuzzing parameters to obtain the processed first channel data;

[0035] The target image signal display module is used to merge the processed first channel data and the second channel data corresponding to the second channel to obtain the image signal to be processed, and to compensate for the brightness loss of the image signal to be processed by gamma correction technology to obtain the target image signal, and to display the target image signal through the target display.

[0036] Thirdly, this application provides an electronic device, comprising:

[0037] Memory, used to store computer programs;

[0038] A processor is used to execute the computer program to implement the aforementioned image processing method.

[0039] Fourthly, this application provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned image processing method.

[0040] In this application, the input raw image signal is first acquired through a first preset interface, and the raw image signal is decomposed into independent channels in the target color space. Then, each independent channel is divided into a first channel and a second channel, and the first channel is blurred based on the target blur parameter to obtain the processed first channel data. Finally, the processed first channel data and the second channel data corresponding to the second channel are merged to obtain the image signal to be processed, and the brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, and the target image signal is displayed through the target display. As can be seen from the above, this application first uses a first preset interface to acquire the input raw image signal and performs corresponding decomposition operations on the raw image signal, thereby mapping the raw image signal to each independent channel under the target color space; then, each independent channel is divided into a first channel and a second channel, and the first channel is blurred using the target blurring parameter to obtain the processed first channel data; then, the processed first channel data is merged with the second channel data of the second channel to form the image signal to be processed; finally, the brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, which is then displayed through the target display. In this way, by blurring the first channel, this application can prevent some of the light output from the screen from participating in imaging, and the unblurred channel data forms a clear imaging pattern, so that when the pattern displayed on the screen is imaged on the retina, the ratio between the light intensity participating in imaging and the light intensity not participating in imaging is reduced. At the same time, this application affects the actual color of the image, but has no impact on display scenarios with a lot of text reading, and can reduce visual fatigue and slow down the growth of the axial length of the eye, making it suitable for reading and learning scenarios for children, adolescents and other people with high myopia rates. This application applies to electronic display devices such as computer monitors and mobile terminal screens. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0042] Figure 1 A flowchart of an image processing method provided in this application;

[0043] Figure 2 This application provides a schematic diagram of a specific text image processing result;

[0044] Figure 3This application provides a specific schematic diagram of a graphic image processing result;

[0045] Figure 4 This application provides a schematic diagram of a specific landscape image processing result;

[0046] Figure 5 A flowchart of a specific image processing method provided in this application;

[0047] Figure 6 A schematic diagram of an image processing device provided in this application;

[0048] Figure 7 This application provides a structural diagram of an electronic device. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In the field of display control technology, myopia prevention has become an important direction in the research and development of electronic display devices. Traditional myopia prevention displays mostly use brightness adjustment, color temperature adjustment, or blue light filtering technology, but these methods fail to optimize the light intensity distribution for retinal imaging. Some studies have attempted to change the screen's light-emitting characteristics through optical films, but such solutions have significant drawbacks: on the one hand, hardware modification costs are high and it is difficult to adapt to mainstream display devices; on the other hand, the films can cause display brightness decay and color distortion, seriously affecting visual effects. In addition, existing color processing technologies, such as RGB channel separation, are mainly used in image enhancement and color restoration, and have not yet been integrated with the needs of visual health protection. To this end, this application provides an image processing solution that can use image processing technology to increase the proportion of non-imaging light, i.e., ambient light, in the displayed image while ensuring clear visual recognition of the displayed content, thereby improving the problems of eye fatigue and myopia progression.

[0051] See Figure 1 As shown, an embodiment of the present invention discloses an image processing method, which may include:

[0052] Step S11: Obtain the input raw image signal through the first preset interface, and decompose the raw image signal to decompose the raw image signal into each independent channel in the target color space.

[0053] In this embodiment, the original image signal can first be acquired through a first preset interface, which includes, but is not limited to, an HDMI (High Definition Multimedia Interface) interface and a VGA (Video Graphics Array) interface. Then, the original image signal can be decomposed into independent channels in the target color space. Specifically, as shown below:

[0054] In a first specific embodiment, the original image signal can be decomposed into independent channels in the RGB color space. That is, the original image signal can be decomposed into the red (R) channel, green (G) channel, and blue (B) channel in the RGB color space.

[0055] In a second specific embodiment, the original image signal can be decomposed into independent channels in the CMYK color space. That is, the original image signal can be decomposed into the cyan (C) channel, magenta (M) channel, yellow (Y) channel, and black (K) channel in the CMYK color space.

[0056] It should be noted that when decomposing the original image signal into the target color space, the RGB color space is preferred if hardware compatibility is a primary concern. In this embodiment, the original image signal is preferably decomposed into the red, green, and blue channels of the RGB color space.

[0057] Step S12: Divide each independent channel into a first channel and a second channel, and perform fuzzing processing on the first channel based on the target fuzzing parameters to obtain the processed first channel data.

[0058] In this embodiment, before performing blurring processing on the first channel based on the target blurring parameters to obtain the processed first channel data, the process may further include: firstly, acquiring current ambient light data through an ambient light sensor; the ambient light data includes light intensity and spectral characteristics; then, determining the target distance between the user's eyes and the target display through a distance sensor; finally, determining the blur radius based on the ambient light data and the target distance using a preset algorithm, and determining the target blurring parameters based on the blur radius. Specifically, the ambient light sensor can first collect the light intensity and spectral characteristics of the current environment and convert them into electrical signals for transmission through an interface. Then, the distance sensor can calculate the distance by transmitting and receiving the time difference or phase difference of the reflected signal, thereby measuring the distance between the user's eyes and the display. Distance sensors include, but are not limited to, infrared sensors, ultrasonic sensors, or TOF (Time of Flight) sensors. Infrared sensors are low-cost, but their accuracy is affected by ambient light. TOF sensors have high accuracy, strong anti-interference capabilities, and are suitable for complex environments. It should be noted that, to ensure data accuracy, the sensor needs to be calibrated before use to eliminate individual differences and environmental interference. Then, based on ambient light data and target distance, the blur radius can be calculated using mathematical models or machine learning algorithms. Finally, the calculated blur radius is mapped to specific target blur parameters, including but not limited to blur type and blur radius.

[0059] It should be noted that in this embodiment, the selected channels need to be blurred. The core intention of this embodiment is to blur the color regions corresponding to short wavelength bands in the image, while leaving other color regions unblurred. Then, the blurred short wavelength band color regions are merged with the unprocessed color regions. The final image effect is as follows: the unprocessed color regions remain clear, providing clear image information for vision, while the short wavelength band color regions, due to blurring, cannot form a clear image on the retina, thus achieving the desired visual effect. In this embodiment, considering the characteristics of human vision, the above-mentioned division of each independent channel into a first channel and a second channel may include: if the independent channel is an independent channel in the RGB color space, then the independent channel is a blue channel, a red channel, and a green channel; then, based on the characteristics of human vision, the blue channel among the independent channels is determined as a short wavelength light channel, and the red and green channels among the independent channels are determined as non-short wavelength light channels; finally, the short wavelength light channel is determined as the first channel, and the non-short wavelength light channel is determined as the second channel. Specifically, in this embodiment, blurring strategies can be combined with physiological optical characteristics. If the original image signal is decomposed into the red, green, and blue channels in the RGB color space, then considering the differences in human eye focusing on different wavelengths of light, such as shorter wavelengths being more prone to defocusing, the shorter wavelength channels in the RGB color space can be identified. It is known that the blue channel in the RGB color space is a shorter wavelength channel; therefore, the blue channel can be designated as the first channel, and the red and green channels as the second channels. The blue channel is then blurred based on the target blurring parameters.

[0060] Furthermore, to more accurately meet specific visual effect requirements, the region segmentation criteria for blurring are not fixed but dynamically adjusted based on the brightness information displayed by both short and long wavelengths of the image itself. This ensures that the light intensity of the blurred color region reaches a target proportion of the total light intensity. Therefore, when the brightness ratio of the short-wavelength blue band of the displayed pattern is too low, the above-mentioned division of each independent channel into a first channel and a second channel may include: if the independent channel is an independent channel in the RGB color space, then determine the display ratio of each color in the original image signal, and determine the color with the largest display ratio as the target reference color; determine the brightness of each independent channel corresponding to the target reference color, and determine the independent channel with the largest brightness as the target channel; if the target channel is not a blue channel, then determine the target channel and the blue channel as the first channel, determine the remaining independent channels in the RGB color space other than the first channel as the second channel, and set a first blur radius corresponding to the blue channel and a second blur radius corresponding to the target channel, so as to blur the blue channel based on the first blur radius and blur the target channel based on the second blur radius; the first blur radius is greater than the second blur radius; if the target channel is a blue channel, then directly determine the blue channel as the first channel, determine the red and green channels in the RGB color space as the second channel, and determine a third blur radius corresponding to the blue channel, so as to blur the blue channel based on the third blur radius. Specifically, if the original image signal is decomposed into red, green, and blue channels in the RGB color space, the display ratio of each color in the original image can be determined by analyzing the separated signals. For example, when the original image mainly displays yellow, since yellow is a mixture of red and green in the RGB color space, the brightness of the blue channel is extremely low, close to 0. If only the characteristics of human vision are considered, and blurring is only applied to the blue channel, the effect of blurring on the overall light intensity is limited due to the low brightness of the blue channel itself, and it cannot effectively reduce the imaging light ratio. Since the green channel in the yellow image has a high brightness, close to 255, the green and blue channels can be determined as the first channel, the red channel as the second channel, and the first blur radius corresponding to the blue channel and the second blur radius corresponding to the green channel can be determined. Then, blurring is applied to the blue channel based on the blur parameters corresponding to the first blur radius, and blurring is applied to the green channel based on the blur parameters corresponding to the second blur radius, so that some green light becomes "ambient light" and does not participate in retinal imaging, thereby further reducing the imaging light intensity. It is known that the human eye's focusing characteristics for green light are between those for red and blue light. Moderately blurring the green channel can avoid color imbalance caused by processing only the blue channel, and at the same time help reduce retinal defocus signals.

[0061] In this embodiment, the above-mentioned blurring of the first channel based on the target blurring parameter to obtain the processed first channel data may include: performing Gaussian blurring on the first channel based on a two-dimensional Gaussian function and the target blurring parameter to obtain the processed first channel data; or, performing low-pass filtering on the first channel based on a preset convolution kernel and the target blurring parameter to obtain the processed first channel data. Specifically, in one specific implementation, pixel weights can be calculated based on a two-dimensional Gaussian function, and Gaussian blurring can be performed on the first channel based on the pixel weights and the target blurring parameter. In another specific implementation, a convolution kernel, such as a 3×3 mean kernel, can be used to smooth high-frequency signals, thereby performing low-pass filtering on the first channel. It should be noted that in this embodiment, a 50-pixel Gaussian blur is preferably performed on the blue channel in the RGB color space. That is, the preferred solution in this embodiment is to blur the blue channel in the RGB color space, but clearly display the red and green channels in the RGB color space. The purpose is to ensure the clear display of red and green light while converting some blue light into ambient light, thereby reducing the ratio of the imaging light of the displayed pattern to the ambient light. If the blue proportion of the displayed pattern is too low, the channel separation ratio and spectral characteristics should be adjusted according to the overall tonal characteristics of the image.

[0062] It should be noted that this embodiment can also incorporate lightweight neural networks, such as MobileNet (Mobile Neural Network), to identify high-frequency detail areas and perform local channel blurring. When a text area is identified, only the blue background channel can be blurred to preserve the clarity of the text area; when a skin color area is identified, the blur intensity of the green channel can be automatically reduced to avoid skin color distortion.

[0063] Step S13: Merge the processed first channel data and the second channel data corresponding to the second channel to obtain the image signal to be processed, and compensate for the brightness loss of the image signal to be processed by gamma correction technology to obtain the target image signal, and display the target image signal through the target display.

[0064] In this embodiment, after blurring the first channel, the processed first channel data and the corresponding second channel data need to be recombined into a reconstructed signal to obtain the image signal to be processed. Then, to maintain the overall visual effect of the image, the above-mentioned compensation for the brightness loss of the image signal to be processed using gamma correction technology to obtain a target image signal, and the display of the target image signal through a target display, can include: first, determining a target compensation coefficient based on ambient light data collected by an ambient light sensor, and determining the brightness loss corresponding to the image signal to be processed based on the target compensation coefficient and the processed first channel data; then, determining a target compensation value based on the brightness loss, and compensating the image signal to be processed based on the target compensation value using the gamma correction algorithm to obtain the target image signal; finally, converting the target image signal into a corresponding analog voltage, transmitting the analog voltage to the target display through a second preset interface, and displaying the target image signal through the target display. Specifically, since blurring the first channel will cause a decrease in the overall brightness of the image signal to be processed, this embodiment can determine the brightness loss corresponding to the image signal to be processed and compensate for the brightness of the image signal to be processed using a gamma correction algorithm, as shown below:

[0065] ;

[0066] Where Y is the original brightness value of the image signal to be processed. This is the compensation coefficient, which can be determined based on ambient light data; the default value is 0.1. This is the original value of the first channel. This is the value after blurring the first channel. The target image signal is its brightness value. The target image signal is then converted into an analog voltage via a DAC (Digital-to-Analog Converter) and transmitted to the target display through a second preset interface. The target display shows the target image signal. This second preset interface includes, but is not limited to, an LVDS (Low Voltage Differential Signaling) interface or an eDP (Embedded DisplayPort). Therefore, this embodiment can be implemented through software drivers or hardware modules. The software can process calculations using the computer's graphics card or central processing unit, while the hardware module can be connected between the computer's output and the display screen.

[0067] It should be noted that, see Figure 2As shown, the original text image signal is decomposed into independent channels in the RGB color space, and the blue channel is blurred. Finally, the blurred blue channel data is merged with the other unprocessed channel data to output the target text image signal. See also Figure 3 As shown, Figure 3 In the image, (a) represents the original graphic signal. Figure 3 In the diagram, (b) represents the target image signal after processing the original image signal. See also... Figure 4 As shown, Figure 4 (a) in the image represents the original landscape image signal. Figure 4 In Figure (b), the target landscape image signal is processed from the original landscape image signal. It can be seen that this embodiment can reduce the ratio of imaging light to ambient light in the displayed pattern from 1:0 to 6:1, 5:1, 4:1, 3:1, 2:1, or lower. In this way, this embodiment can solve the problem of eye strain and increased myopia caused by excessively high ratios of imaging light and non-imaging light in specific areas of the retina due to full-spectrum high-definition output in existing displays.

[0068] In one specific implementation, see Figure 5 As shown, the specific process of the image processing method can be as follows: First, the input image signal is received via the HDMI interface, and then RGB separation is performed by the FPGA (Field-Programmable Gate Array) chip; then, the blue channel is subjected to a 50-pixel Gaussian blur; subsequently, the blurred blue channel data is recombined with other unprocessed channel data to form a reconstituted signal; then, the brightness of the reconstituted signal is compensated using the following formula:

[0069] ;

[0070] in, This is the original value of the blue channel. This is the blurred value for the blue channel. Finally, it is output to the LCD panel, and in actual measurements, it can reduce the ratio of imaging light to ambient light to 2:1.

[0071] In another specific implementation, the image processing method can be as follows: receiving an image signal; analyzing the separated signals of the input image; when the brightness of the blue channel decreases, such as when the displayed image is mainly yellow; performing a 50-pixel Gaussian blur on the blue channel and a 20-pixel Gaussian blur on the green channel; recombining the blurred blue channel data, the blurred green channel data, and the unprocessed channel data into a reconstituted signal; performing brightness compensation on the reconstituted signal; and outputting it to a display. Actual measurements show that this can reduce the ratio of imaging light to ambient light to 3:1.

[0072] As can be seen from the above, in this embodiment, the input original image signal is first obtained through the first preset interface, and the original image signal is decomposed to decompose the original image signal into independent channels in the target color space; then, each independent channel is divided into a first channel and a second channel, and the first channel is blurred based on the target blur parameter to obtain the processed first channel data; finally, the processed first channel data and the second channel data corresponding to the second channel are merged to obtain the image signal to be processed, and the brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, and the target image signal is displayed through the target display. As can be seen from the above, in this embodiment, the input raw image signal is first obtained using the first preset interface, and the raw image signal is decomposed accordingly to map the raw image signal to each independent channel in the target color space; then, each independent channel is divided into a first channel and a second channel, and the first channel is blurred using the target blur parameter to obtain the processed first channel data; then, the processed first channel data is merged with the second channel data of the second channel to form the image signal to be processed; finally, the brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, which is then displayed on the target display. In this way, by blurring the first channel in this embodiment, the light output from the screen does not participate in imaging, and the unblurred channel data forms a clear imaging pattern, so that the pattern displayed on the screen reduces the ratio between the light intensity participating in imaging and the light intensity not participating in imaging when the retina is imaged. At the same time, this application affects the actual color of the image, but has no impact on display scenarios with a lot of text reading, and can reduce visual fatigue and slow down the growth of the axial length of the eye, making it suitable for reading and learning scenarios for children, adolescents and other people with high myopia rates. This embodiment is applicable to electronic display devices such as computer monitors and mobile terminal screens.

[0073] Accordingly, see Figure 6 As shown in the illustration, this application also provides an image processing apparatus, which may include:

[0074] The image signal decomposition module 11 is used to acquire the input original image signal through a first preset interface and decompose the original image signal to decompose the original image signal into independent channels in the target color space.

[0075] The channel fuzzing module 12 is used to divide each of the independent channels into a first channel and a second channel, and to perform fuzzing processing on the first channel based on the target fuzzing parameters to obtain the processed first channel data;

[0076] The target image signal display module 13 is used to merge the processed first channel data and the second channel data corresponding to the second channel to obtain the image signal to be processed, and to compensate for the brightness loss of the image signal to be processed by gamma correction technology to obtain the target image signal, and to display the target image signal through the target display.

[0077] In some specific embodiments, the image signal decomposition module 11 may include:

[0078] The first image signal decomposition unit is used to decompose the original image signal to decompose the original image signal into independent channels in the RGB color space.

[0079] The second image signal decomposition unit is used to decompose the original image signal into independent channels in the CMYK color space.

[0080] In some specific embodiments, the image processing apparatus may further include:

[0081] An ambient light data acquisition module is used to acquire current ambient light data through an ambient light sensor; the ambient light data includes light intensity and spectral characteristics.

[0082] A target distance determination module is used to determine the target distance between the user's eyes and the target display via a distance sensor;

[0083] The target blur parameter determination module is used to determine the blur radius based on the ambient light data and the target distance using a preset algorithm, and to determine the target blur parameter based on the blur radius.

[0084] In some specific embodiments, the channel blurring module 12 may include:

[0085] A Gaussian blur unit is used to perform Gaussian blurring on the first channel based on a two-dimensional Gaussian function and the target blur parameters to obtain the processed first channel data.

[0086] The low-pass filtering processing unit is used to perform low-pass filtering processing on the first channel based on a preset convolution kernel and the target blurring parameters to obtain the processed first channel data.

[0087] In some specific embodiments, the channel blurring module 12 may include:

[0088] The condition judgment unit is used to determine if the independent channel is an independent channel in the RGB color space, and then the independent channel is the blue channel, the red channel, and the green channel.

[0089] A short-wavelength light channel determination unit is used to determine the blue channel in the independent channels as a short-wavelength light channel based on the visual characteristics of the human eye, and to determine the red channel and the green channel in the independent channels as non-short-wavelength light channels.

[0090] The first channel division unit is used to determine the shortwave optical channel as the first channel and the non-shortwave optical channel as the second channel.

[0091] In some specific embodiments, the channel blurring module 12 may include:

[0092] The target reference color determination unit is used to determine the display ratio of each color in the original image signal if the independent channel is an independent channel in the RGB color space, and to determine the color with the largest display ratio as the target reference color.

[0093] The target channel determination unit is used to determine the brightness of each independent channel corresponding to the target reference color, and to determine the independent channel with the highest brightness as the target channel;

[0094] The second channel partitioning unit is configured to, if the target channel is not a blue channel, determine the target channel and the blue channel as the first channel, determine the remaining independent channels in the RGB color space other than the first channel as the second channel, and set a first blur radius corresponding to the blue channel and a second blur radius corresponding to the target channel, so as to perform blur processing on the blue channel based on the first blur radius and blur processing on the target channel based on the second blur radius; the first blur radius is greater than the second blur radius.

[0095] The third channel division unit is used to directly determine the blue channel as the first channel if the target channel is the blue channel, determine the red and green channels in the RGB color space as the second channel, and determine the third blur radius corresponding to the blue channel so as to perform blur processing on the blue channel based on the third blur radius.

[0096] In some specific embodiments, the target image signal display module 13 may include:

[0097] A brightness loss determination unit is used to determine a target compensation coefficient based on ambient light data collected by an ambient light sensor, and to determine the brightness loss corresponding to the image signal to be processed based on the target compensation coefficient and the processed first channel data.

[0098] The target image signal determination unit is used to determine a target compensation value based on the brightness loss, and to compensate the image signal to be processed based on the target compensation value using the gamma correction algorithm to obtain the target image signal.

[0099] The target image signal display unit is used to convert the target image signal into a corresponding analog voltage, transmit the analog voltage to the target display through a second preset interface, and display the target image signal through the target display.

[0100] Furthermore, embodiments of this application also disclose an electronic device, Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the image processing method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0101] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0102] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0103] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs capable of performing the image processing methods executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0104] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned disclosed image processing method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0105] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0106] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0108] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0109] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An image processing method, characterized in that, include: The input raw image signal is obtained through the first preset interface, and the raw image signal is decomposed to decompose the raw image signal into each independent channel in the target color space. Each independent channel is divided into a first channel and a second channel, and the first channel is blurred based on the target blurring parameters to obtain the processed first channel data; The processed first channel data and the second channel data corresponding to the second channel are merged to obtain the image signal to be processed, and the brightness loss of the image signal to be processed is compensated by gamma correction technology to obtain the target image signal, and the target image signal is displayed through the target display. The step of dividing each of the independent channels into a first channel and a second channel includes: If the independent channel is an independent channel in the RGB color space, then the display ratio of each color in the original image signal is determined, and the color with the largest display ratio is determined as the target reference color; the brightness of each independent channel corresponding to the target reference color is determined, and the independent channel with the largest brightness is determined as the target channel; if the target channel is not the blue channel, then the target channel and the blue channel are determined as the first channel, the remaining independent channels in the RGB color space other than the first channel are determined as the second channel, and a first blur radius corresponding to the blue channel and a second blur radius corresponding to the target channel are set, so that the blue channel is blurred based on the first blur radius, and the target channel is blurred based on the second blur radius; the first blur radius is greater than the second blur radius; if the target channel is the blue channel, then the blue channel is directly determined as the first channel, the red channel and the green channel in the RGB color space are determined as the second channel, and a third blur radius corresponding to the blue channel is determined, so that the blue channel is blurred based on the third blur radius; The process of compensating for the brightness loss of the image signal to be processed using gamma correction technology to obtain the target image signal, and displaying the target image signal on the target display, includes: The target compensation coefficient is determined based on the ambient light data collected by the ambient light sensor, and the brightness loss corresponding to the image signal to be processed is determined based on the target compensation coefficient and the processed first channel data. Based on the brightness loss, a target compensation value is determined, and the target image signal is obtained by compensating the image signal to be processed based on the target compensation value using the gamma correction algorithm. The target image signal is converted into a corresponding analog voltage, and the analog voltage is transmitted to the target display through a second preset interface, and the target image signal is displayed through the target display.

2. The image processing method according to claim 1, characterized in that, The step of decomposing the original image signal into independent channels in the target color space includes: The original image signal is decomposed into independent channels in the RGB color space; Alternatively, the original image signal can be decomposed into independent channels in the CMYK color space.

3. The image processing method according to claim 1, characterized in that, Before performing fuzzing processing on the first channel based on the target fuzzing parameters to obtain the processed first channel data, the method further includes: Current ambient light data is acquired using an ambient light sensor; the ambient light data includes light intensity and spectral characteristics. The target distance between the user's eyes and the target display is determined using a distance sensor; The blur radius is determined based on the ambient light data and the target distance using a preset algorithm, and the target blur parameters are determined based on the blur radius.

4. The image processing method according to claim 1, characterized in that, The process of blurring the first channel based on the target blurring parameters to obtain the processed first channel data includes: Gaussian blurring is performed on the first channel based on the two-dimensional Gaussian function and the target blurring parameters to obtain the processed first channel data; Alternatively, the first channel can be low-pass filtered based on a preset convolution kernel and the target blurring parameters to obtain the processed first channel data.

5. The image processing method according to claim 1, characterized in that, The process of dividing each of the independent channels into a first channel and a second channel includes: If the independent channel is an independent channel in the RGB color space, then the independent channel is the blue channel, the red channel, and the green channel; Based on the characteristics of human vision, the blue channel in the independent channel is identified as a short-wavelength light channel, and the red and green channels in the independent channel are identified as non-short-wavelength light channels; The shortwavelength optical channel is designated as the first channel, and the non-shortwavelength optical channel is designated as the second channel.

6. An image processing apparatus, characterized in that, include: The image signal decomposition module is used to acquire the input raw image signal through a first preset interface and decompose the raw image signal to decompose the raw image signal into independent channels in the target color space. The channel fuzzing module is used to divide each of the independent channels into a first channel and a second channel, and to perform fuzzing processing on the first channel based on the target fuzzing parameters to obtain the processed first channel data; The target image signal display module is used to merge the processed first channel data and the second channel data corresponding to the second channel to obtain the image signal to be processed, and to compensate for the brightness loss of the image signal to be processed by gamma correction technology to obtain the target image signal, and to display the target image signal through the target display. Specifically, the channel blurring module is used to: if the independent channel is an independent channel in the RGB color space, determine the display ratio of each color in the original image signal and determine the color with the largest display ratio as the target reference color; determine the brightness of each independent channel corresponding to the target reference color and determine the independent channel with the largest brightness as the target channel; if the target channel is not the blue channel, determine the target channel and the blue channel as the first channel, determine the remaining independent channels in the RGB color space other than the first channel as the second channel, and set a first blur radius corresponding to the blue channel and a second blur radius corresponding to the target channel, so as to blur the blue channel based on the first blur radius and blur the target channel based on the second blur radius; the first blur radius is greater than the second blur radius; if the target channel is the blue channel, directly determine the blue channel as the first channel, determine the red and green channels in the RGB color space as the second channel, and determine a third blur radius corresponding to the blue channel, so as to blur the blue channel based on the third blur radius; The target image signal display module includes: A brightness loss determination unit is used to determine a target compensation coefficient based on ambient light data collected by an ambient light sensor, and to determine the brightness loss corresponding to the image signal to be processed based on the target compensation coefficient and the processed first channel data. The target image signal determination unit is used to determine a target compensation value based on the brightness loss, and to compensate the image signal to be processed based on the target compensation value using the gamma correction algorithm to obtain the target image signal. The target image signal display unit is used to convert the target image signal into a corresponding analog voltage, transmit the analog voltage to the target display through a second preset interface, and display the target image signal through the target display.

7. An electronic device, characterized in that, The electronic device includes a processor and a memory; wherein the memory is used to store a computer program, which is loaded and executed by the processor to implement the image processing method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store a computer program, which, when executed by a processor, implements the image processing method as described in any one of claims 1 to 5.

Images