26results about How to "Improve local contrast" patented technology

The invention relates to a display screen backlight brightness adjusting method and system based on a Mini QLED technology. The method comprises the following steps: dividing a display image signal into a brightness signal, a chrominance signal and a saturation signal, and generating image feature data; performing sub-pixel-level boundary detection to obtain a plurality of initial backlight sub-regions, and calculating region brightness distribution data; executing region clustering operation and region merging to obtain a plurality of target backlight sub-regions and calculating backlight partition mapping data; carrying out nonlinear feature transformation, and calculating backlight unit control data of the Mini LED backlight unit; outputting the backlight compensation amount of each target backlight sub-region by adopting a deep Q network; and performing real-time feedback correction, calculating a deviation value between an actual display parameter and a target parameter of each target backlight sub-region, and outputting a backlight optimization control signal. According to the invention, the backlight brightness is accurately adjusted, and the consistency of the display effect is improved.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention discloses an infrared small target detection algorithm based on multi-scale differential contrast enhancement, which belongs to the technical field of image segmentation and comprises the following steps: acquiring an original image containing a single-frame infrared small target; performing multi-scale Gaussian blur processing on the original image to generate a series of blurred images with different scales; calculating the difference of the adjacent scale blurred images, and generating a multi-scale Gaussian difference image; detecting a local maximum value point in the multi-scale Gaussian difference image; constructing a multi-scale block region for each local maximum value point, and calculating the mean difference between the local maximum value point and eight neighborhoods thereof by using a multi-scale block contrast function; summarizing calculation results of the multi-scale block contrast function to generate a contrast enhanced image; and setting a threshold value according to the average value and the standard deviation of the contrast-enhanced image, and segmenting the contrast-enhanced image according to the threshold value to obtain a final target detection result. The method can effectively detect the infrared small target in the complex background, and has good robustness and accuracy.

The invention relates to the technical field of image transmission, in particular to a remote ultrasonic image transmission system. The remote ultrasonic image transmission system comprises an ultrasonic image feature extraction module, a bandwidth dynamic adaptation module, a real-time annotation information coding module, an HDR image processing module, a network condition monitoring module, an image and annotation synchronous transmission module and a client decoding display module. According to the method, the ultrasonic image is analyzed through a deep learning algorithm, the network bandwidth is dynamically adapted, annotation information transmission is optimized through a differential coding algorithm, the visual effect of the image is improved through local contrast enhancement and HDR processing, synchronous transmission of the image and the annotation is guaranteed, and the quality and efficiency of remote medical image transmission are improved; accurate display of important details is guaranteed through accurate processing of diagnosis-related key areas, diagnosis accuracy is improved, network fluctuation is effectively coped with through a dynamic adjustment strategy of network bandwidth, transmission stability is guaranteed, and accessibility and quality of medical services are remarkably improved.

The invention discloses a diseased fish detection method and device, and belongs to the technical field of computer vision detection. In order to solve the problems that in the prior art, diseased fish disease detection efficiency is low, accuracy is not high, and real-time detection cannot be achieved, a diseased fish detection model is constructed, and the diseased fish detection model comprises a backbone network, a neck network and a detection head network; the method comprises the following steps: inputting a diseased fish image into a backbone network based on a spatial deep convolutional neural network for feature extraction to obtain a visual feature map, and then inputting the visual feature map into a contrast-enhanced attention transfer structure of a neck network for feature fusion processing to obtain a fused feature map; and the fusion feature map is input into a detection head network, and a sick fish target detection result is obtained through detection of a multi-head prediction small target detection algorithm, so that fish school disease detection is performed in real time, the detection efficiency and detection precision of fish diseases are effectively improved, and the method has relatively high practicability and application universality.

The invention relates to the technical field of retina image quality evaluation, and particularly discloses a retina image quality evaluation method based on double-path frequency domain cross attention fusion, comprising the following steps: step S1, acquiring an RGB image; s2, preprocessing the quality of the BGR image through a contrast limit adaptive histogram equalization algorithm to obtain a CLAHE image; s3, inputting the RGB image and the CLAHE image into a dual-path frequency domain DenseNet121 network, and extracting features of the RGB image and the CLAHE image through a frequency domain attention mechanism algorithm; s4, refining the extracted features through a double cross attention algorithm, and obtaining integration of the BGR image and the CLAHE image; and S5, obtaining a quality evaluation result through a linear classifier. According to the method, important retina image structure details are reserved, and the adaptability of the model to different illumination conditions is also improved.

The invention discloses an aviation airborne navigation image target identification method based on harmony search of spatial entropy, which comprises the following steps of: calculating an image gray space distribution entropy value by using position distribution information of an image gray space, mapping the image gray space distribution entropy value into a uniform distribution function, and performing 2D-DCT (two-dimensional discrete cosine transform), transformation system weighting and inverse 2D-DCT linear transformation processing to obtain a harmony search result. The global and local contrast of an original image is enhanced at the same time, the harmony search algorithm of the maximum entropy is applied in combination, although the algorithm is similar to a genetic algorithm, a multi-point mating mode is adopted in the process of searching a new solution in the field, so that the new solution is more diversified, and the possibility that the algorithm jumps out of a local optimal solution is improved.

The invention relates to the field of data processing, in particular to an audio and video data processing method and device, equipment and a storage medium. The method comprises the following steps: acquiring to-be-processed audio data and to-be-processed video data; performing multi-scale wavelet transform decomposition on the to-be-processed audio data, performing nonlinear mapping reconstruction, and constructing distortion correction audio data; carrying out multi-time-window segmentation processing on the distortion correction audio data, carrying out time sequence characteristic evolution fitting, and constructing an audio time sequence semantic characteristic graph; performing local contrast enhancement processing on the to-be-processed video data to generate a local enhanced video; performing super-resolution fusion reconstruction on the local enhanced video to construct a super-definition reconstructed video; frame-by-frame time sequence decomposition is carried out on the super-definition reconstructed video, optical flow displacement tracking between adjacent frames is carried out, and a dynamic video frame sequence is constructed. According to the invention, the time synchronization of audio and video data is improved, and the time deviation delay of audio and video is optimized.

The invention discloses a screen copying identification method, device and equipment, and the method comprises the steps: carrying out the LBP feature extraction of a target image through a plurality of preset pixel radiuses, and obtaining a plurality of LBP feature data sets; scaling the target image by using a scaling factor to obtain a target scaled image under the scaling factor; for each target scaled image, executing the steps S131-132: S131, performing edge detection and frequency domain analysis on the target scaled image to obtain the moire feature and moire intensity of the target scaled image; s132, inputting a plurality of LBP feature data sets and the moire features and moire intensity of the target scaled image into a preset multi-feature input convolutional neural network to obtain a copying probability and a non-copying probability of the target scaled image; and determining whether the target image is copied or not according to the copying probabilities and the non-copying probabilities of the plurality of target scaled images. The invention belongs to the field of image copying recognition. According to the invention, the accuracy of image copying identification can be improved.

This application provides an image processing method, apparatus, device, storage medium, and program product. The method includes: acquiring an RGB image; converting the RGB image into a luminance image; determining a luminance gain value for each pixel in the RGB image based on the luminance image; and processing the RGB image based on the luminance gain values ​​to obtain a processed RGB image. This improves the local contrast of the image.

This invention belongs to the field of underwater robot control technology, specifically providing a dual-field-of-view cooperative perception and adaptive weighted localization method and system for underwater robots in confined spaces. The method utilizes a pre-calibrated heterogeneous dual-field-of-view camera system on the robot to acquire main and auxiliary field-of-view images. Image enhancement is performed through red channel compensation and contrast-limited adaptive histogram equalization, and image quality indices are calculated. An analytical pose estimation algorithm based on microplanes is employed to detect visual markers and calculate their poses, obtaining two sets of camera pose observations. The two sets of observations are then weighted and fused using calculated fusion weights to obtain the robot's center visual pose. An error-state Kalman filter is constructed, using the visual pose as the observation input, and dynamically adjusting the observation noise covariance matrix based on the fusion weights to output the final pose. This invention can achieve highly robust and high-precision localization in complex underwater environments.

The present invention discloses an aviation airborne navigation image target recognition method based on spatial entropy harmony search. The method utilizes the position distribution information of the image grayscale space to calculate the image grayscale space distribution entropy value, and maps it into a uniform distribution function. The function is then processed by 2D-DCT, transform system weighting and inverse 2D-DCT linear transformation to achieve simultaneous enhancement of the global and local contrast of the original image. The method is combined with the maximum entropy harmony search algorithm. Although the method is similar to the genetic algorithm, it adopts a multi-point mating mode in the process of searching for new solutions in the field, so that the new solutions are more diverse and the possibility of the algorithm jumping out of the local optimal solution is improved.

The present invention provides a method for compressing the dynamic range of an infrared image, comprising: obtaining a high dynamic range infrared image I output by an infrared detector; src ; Use platform histogram equalization algorithm to process infrared image I src Perform first-level compression to obtain the first-level mapped histogram image I0 dst ; Based on linear dynamic compression algorithm and histogram mapping image I0 dst Perform secondary compression to obtain the secondary mapped histogram image I dst The infrared image obtained by the present invention contains richer information, has higher local contrast, and gives people a better visual experience.

The invention belongs to the technical field of target detection, and discloses a weak and small target detection method and system and a storage medium, and the method comprises the steps: calculating the gray distribution-based local contrast CM of a to-be-detected image at a current scale; fitting local gray level distribution of the image to be detected by adopting a two-dimensional discrete orthogonal polynomial; respectively solving partial derivatives of the fitted local gray level distribution relative to rows and columns of the to-be-detected image to obtain directional derivatives of the to-be-detected image in vertical and horizontal directions, and further obtaining a directional derivative of any angle; extracting and fusing the features of each pixel point of the image to be detected on the directional derivative of the corresponding angle to obtain the directional derivative feature GM of the image to be detected; fusing the CM and the GM to obtain a saliency map of the to-be-detected image under the current scale; fusing the saliency maps under each scale to serve as a final saliency map S; and segmenting the saliency map S to locate the position of the target. The method of the invention has high accuracy and low false alarm rate for weak and small targets.

This invention discloses an image processing method based on wavelet transform, belonging to the field of image processing technology. The method includes: converting the RGB values ​​of the acquired image to Lab space and separating the L, a, and b channels; selecting wavelet transform and choosing the order of wavelet transform according to the image size; applying wavelet transform to the L channel to obtain the image coefficients of each layer; calculating the mean and variance of the image coefficients of each layer; and calculating the adjustment coefficient G of each layer. i The adjustment coefficient G is applied to each layer of the wavelet-transformed image. i Apply adjustment factor G to each image layer. i Then, an inverse wavelet transform is performed to recover the L channel; the processed L channel is restored to the range of [0,255]; the L, a, and b channels are merged and converted to RGB space to obtain the processed image; this method preserves the local details of the image, improves noise robustness, and improves computational efficiency.

The invention relates to the technical field of ultrasonic image processing, and the method comprises the steps: carrying out the calculation of three different feature algorithms on an obtained ultrasonic training image, thereby obtaining a clear ultrasonic feature point image; processing the ultrasonic training image based on a preset first feature algorithm to obtain a first ultrasonic image, processing the first ultrasonic image based on a preset second feature algorithm to obtain a second ultrasonic image, and processing the second ultrasonic image based on a preset third feature algorithm to obtain an enhanced ultrasonic image, the ultrasonic training image is processed through the obtained enhanced ultrasonic image and the initial feature extraction model to obtain the initial feature image to determine the loss function, and the quality of the 2D feature points can be improved through the three feature algorithms, so that the problem that the extraction of the 2D ultrasonic image feature points is not accurate enough is solved.

The invention discloses a multi-light-source color constancy method and system based on color decoupling, multi-light-source color constancy is realized by using a pixel-level multi-light-source color constancy model, and the system comprises a content color perception module CCAM and a contrast coordination module CHAM. CCAM learns scene content color feature priori, scene content colors and illumination colors are separated by providing color features of the content for the model, and light source prediction outside the color gamut is reduced. CHAM uses spatial correlation to make the model more sensitive to the relationship between adjacent features, and uses illumination parallax to guide feature classification; the local edge contrast is improved by enhancing the uniqueness of homogeneous illumination features and the distinctiveness of heterogeneous illumination features; the soft discrimination of similar illumination features is emphasized by distributing fine-grained edge coefficients, and the local contrast is further enhanced. The high-precision multi-light-source color constancy is realized by introducing the advanced semantic information to assist the model in understanding the scene and optimizing the feature representation through the fine-grained feature constraint.

The invention discloses an oral cavity digital impression instrument image acquisition method and system, and the method comprises the steps: carrying out the down-sampling of oral cavity images collected by an oral cavity digital impression instrument, and weighting the frequency of a gray value in each down-sampling image according to the size ratio of each down-sampling image to the oral cavity image, the method comprises the following steps of: performing edge detection on an oral cavity image under gray levels of different gray scales, calculating the prominence degree of a pixel point according to gradient amplitudes of the pixel point under different gray scales and the length of an edge to which the pixel point belongs, correcting the weighting frequency of a gray value, obtaining a mapping interval of the gray value according to the correction frequency, and obtaining the mapping interval of the gray value according to the mapping interval; according to the protruding degree of the pixel point and the gray values of other pixel points in the neighborhood of the pixel point, the mapping gray value of the pixel point is determined from the mapping interval of the gray values of the pixel point, and the mapping gray values of all the pixel points in the oral cavity image form an enhanced oral cavity image so as to reconstruct 3D information of teeth. According to the invention, the accuracy and trueness of 3D information reconstruction are improved.

The embodiment of the present application provides a video contrast enhancement method, device, electronic device and storage medium, the method comprising: acquiring multiple frame images of a video to be processed, cutting the frame images to obtain multiple regional images; determining each regional image as a skin image or a non-skin image based on the first pixel information of each pixel point; determining a cropping threshold and a first adjustment function based on the first pixel information in the non-skin image corresponding to the current frame image and the first pixel information in the regional image corresponding to the previous frame image; performing identity mapping on the skin image, adjusting the non-skin image according to the first adjustment function to obtain a first adjusted image; inputting the first adjusted image into an adjustment model to obtain a second adjusted image; performing interpolation processing on the second adjusted image to obtain a target image, and determining a target video according to the target image; the contrast of each frame image in the video can be enhanced while retaining the real skin color of the character.

This invention discloses a driving method and system for a transparent LCD display based on local dimming, relating to the field of local dimming technology for transparent LCD displays. The method includes: dividing the transparent LCD display into multiple driving zones and collecting the ambient light intensity of each driving zone; establishing a transparent display optical perception model; extracting a set of perception indicators; performing driving solution and outputting a combination of driving parameters for each driving zone; and executing a fusion drive of the transparent LCD display according to the multiple driving parameter combinations. This invention solves the technical problem in existing technologies where the driving of a transparent LCD display is difficult to adapt to the differences in ambient light and dynamic changes in image perception, resulting in poor coordination of display brightness, transparency, and local contrast, and insufficient driving accuracy. It achieves precise driving of each driving zone of the transparent LCD display to adapt to the needs of ambient light and image perception, improving the synergistic optimization effect of display brightness, transparency, and local contrast.

The present invention relates to the field of camera technology, and in particular to a camera rainy day vision optimization method, comprising the following steps: S1, rainy day detection: detecting the current weather conditions through an environmental sensor; S2, image acquisition and preprocessing: using a camera to acquire image data of the current environment; S3, raindrop dynamic prediction and repair: identifying and predicting the area where raindrops appear in advance, and dynamically repairing the image damage caused by raindrops through an image repair algorithm; S4, image enhancement: performing image enhancement on the image data after raindrop repair processing; S5, real-time feedback: feeding back the enhanced image data to a monitoring device in real time; S6, dynamic adjustment: based on the real-time feedback image data, combined with actual environmental changes, evaluating the effect of the image data, and dynamically adjusting according to the result of the effect evaluation. The present invention effectively improves the image quality of the camera in a rainy day environment.

This invention relates to the fields of optical imaging and intelligent image processing technology, and discloses a dual-modal image fusion enhancement method, system, device, and storage medium. The method includes: acquiring initial dual-modal images obtained through coaxial synchronous transient acquisition of the same scene; fusing the initial dual-modal images based on a saliency matrix; performing differentiated adaptive noise reduction processing on the low-frequency and high-frequency components separated from the fused image; performing dynamic anti-reflection compensation processing on the reconstructed high- and low-frequency images; performing a two-dimensional fast Fourier transform and centering the spectrum; constructing a notch mask and a low-frequency protection region at the center of the spectrum to suppress the narrow-band energy peak when a narrow-band energy peak corresponding to sensor row and column noise is identified; and then performing an inverse Fourier transform to output the enhanced image. Through the above methods, this invention can improve image sharpness, local contrast, and stripe suppression, while reducing artifacts generated during the enhancement process.

The application relates to the technical field of image data processing, in particular to a geological remote sensing interpretation method and system for mineral resource exploration, which comprises the following steps: acquiring a geological remote sensing image to be analyzed in a mineral resource area; analyzing the uniformity of the distribution of the pixel gray value of all local areas in the image to be analyzed to obtain the image smoothness; acquiring the LBP value of all pixel points in the image to be analyzed and a historical standard sample image and performing clustering; using a seed growing algorithm to obtain each growth area of each image; determining an image detail evaluation index of the image to be analyzed through the overall distribution of the edge complexity of the growth area of each cluster to which the image to be analyzed and the historical standard sample image belong, obtaining an image enhancement effect quality evaluation, adjusting the wavelet threshold in combination with the image detail evaluation index, and interpreting the enhanced image. The application aims to improve the accuracy of geological remote sensing image interpretation in mineral resource exploration.

The application provides an improved CLAHE short-wave infrared image real-time enhancement method based on FPGA. In view of the problems of low contrast, unobvious detail information and easy amplification of noise of InGaAs short-wave infrared imaging under low illumination, an improved limited contrast adaptive histogram equalization image enhancement algorithm is provided, and the optimal clipping threshold is automatically determined through an adaptive search mechanism, so that the local contrast is enhanced and the noise amplification is inhibited. The application can effectively improve the contrast and detail distinguishability of the short-wave infrared image, reduce the influence of noise, and is suitable for fields of short-wave infrared imaging systems, target identification systems and weak light vision systems.

The invention relates to the technical field of image data processing, in particular to a water conservancy project topographic mapping method based on an unmanned aerial vehicle remote sensing technology, and the method comprises the steps: collecting the image information of a dam at a plurality of positions and a plurality of angles through an unmanned aerial vehicle, so as to obtain a plurality of initial dam images; determining a first target corner point of a hot spot area in the initial dam image, and determining a second target corner point of a floating object area in the initial dam image; removing the first target corner point and the second target corner point to obtain a processed target dam image; and splicing the plurality of target dam images to obtain and output a water conservancy project topographic map. According to the water conservancy project topographic mapping method based on the unmanned aerial vehicle remote sensing technology, the angular points of the hot spot area and the floating object area are accurately removed, in the image matching process, matching errors are reduced, the image splicing quality and accuracy are remarkably improved, and a more accurate and clearer water conservancy project topographic map is obtained.

The present invention relates to a method and system for adjusting the backlight brightness of a display screen based on Mini QLED technology. The method: divides a display image signal into a brightness signal, a chromaticity signal and a saturation signal, and generates image feature data; performs sub-pixel boundary detection to obtain multiple initial backlight sub-regions, and calculates regional brightness distribution data; performs regional clustering operations and regional merging to obtain multiple target backlight sub-regions and calculates backlight partition mapping data; performs nonlinear feature transformation, and calculates the backlight unit control data of the Mini LED backlight unit; uses a deep Q network to output the backlight compensation amount of each of the target backlight sub-regions; performs real-time feedback correction, calculates the deviation value between the actual display parameters and the target parameters of each target backlight sub-region, and outputs a backlight optimization control signal. The implementation of the present invention realizes the precise adjustment of the backlight brightness and improves the consistency of the display effect.