2481 results about "Lightness" patented technology

The invention provides a computer visual defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the steps that according to collected multi-angle images, the overall area of an electric control board is partitioned in combination with illumination conditions and visual angle information; performing image partitioning processing to generate a plurality of image sub-regions, and extracting texture features, brightness distribution features and geometric edge features in each image sub-region; rare defect feature enhancement processing is executed, multi-scale repeated superposition is carried out on low-frequency abnormal textures, and directional extension is carried out on edge fractures; performing difference comparison with the corresponding normal area combination features, and performing normalization correction in combination with the illumination condition and the visual angle information; multi-angle reproducibility analysis is executed, when the same suspected defect is detected at different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; according to the invention, the accuracy of defect detection is improved.

The invention discloses a stainless steel tube surface defect detection method and system. The method comprises the following steps: S1, collecting stainless steel tube scanning images under dark field and bright field illumination; s2, splicing the line scanning images into dark field and bright field cylindrical expanded images, and executing brightness equalization and reflection suppression processing; s3, carrying out pixel-level fusion on the dark field and bright field cylindrical expansion images according to a set weight; s4, inputting the fusion cylinder expansion image into a YOLO network trunk of an integrated Swin Transform module, and extracting a multi-scale feature map; s5, performing feature fusion and bounding box prediction, and outputting a bounding box, confidence and a category label of the defect; s6, performing semantic segmentation, gray segmentation and weighted fusion in the detection frame area to generate a final defect mask; and S7, calculating the area, length, width and centroid coordinates of the defect, and converting the actual size and the spatial position. The stainless steel tube surface defect recognition accuracy and stability are improved.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention relates to the technical field of display screens, in particular to a liquid crystal display screen backlight local dimming method and device, a terminal and a medium. Comprising the steps of obtaining brightness and color distribution data of a display picture; calculating a target brightness value of each backlight block through the dimming algorithm module based on a dynamic partition control strategy and a preset brightness mapping model; wherein the dynamic partition control strategy is used for dividing adjacent backlight blocks into dynamic dimming groups according to brightness characteristics of a picture area on the premise that the number of the backlight blocks is not increased; the backlight control device drives an LED backlight block of the backlight module according to the target brightness value; and monitoring a display effect in real time, and adjusting parameters of the brightness mapping model based on feedback. According to the invention, the problems of cost increase and related derivation caused by increase of the number of partitions in the prior art can be solved.

The invention provides a multi-scene-oriented intelligent light adaptive control method and system, and the method comprises the steps: recognizing the current behavior state of a user based on visual sensor data, predicting the behavior state of the user, and outputting a light control behavior tag; combining the behavior label with a scene type and ambient light intensity to generate a target illumination intention vector; according to the target illumination intention vector, in combination with an illumination control conflict situation, real-time lamp parameters and user space state data, calculating an illumination weight of each lamp to a user, distributing brightness and color temperature parameters, and in combination with the target dimming process duration, generating a lamp control parameter set; and converting the lamp control parameter set into a dimming instruction sequence, performing time synchronization and delay compensation, and issuing the dimming instruction sequence to each lamp for execution. According to the method, accurate mapping from behavior intention to physical illumination is realized, and the method has extremely high universality, deployability and engineering controllability.

The invention discloses an endoscope multi-region semantic perception time domain fusion adaptive white balance algorithm. The algorithm comprises the following steps: acquiring an endoscope image; performing tissue region division on the endoscope image through a lightweight semantic segmentation network to obtain a plurality of region images; converting each regional image from an RGB space to a YCbCr color space, separating a brightness channel Y from chrominance channels Cb and Cr, and calculating a channel mean value of the chrominance channels Cb and Cr; calculating the average offset of the chrominance channels Cb and Cr according to the calculated channel average of the chrominance channels Cb and Cr; performing white point detection on each regional image to obtain an RGB three-channel mean value; combining the calculated mean offset of the chromaticity channels Cb and Cr, the RGB three-channel mean obtained by white point detection of each regional image and a statistical index to form a multi-dimensional regional color feature vector; and fusing historical frame features with the regional color feature vector to obtain a feature vector based on space-time fusion.

The invention relates to the technical field of panel display brightness adjustment, and discloses a panel display module brightness adjustment method based on multi-sensor fusion. The method comprises the following steps: collecting multi-source sensor data of a panel display module; processing multi-source sensor data by using a dynamic feature fusion algorithm, distributing dynamic weights according to the real-time confidence of each sensor data source through a weighted fusion mechanism, and generating a brightness adjustment feature set; constructing a dynamic brightness adjustment index table, representing display partition brightness parameters by nodes, representing visual relevance among display partitions by edges, and updating node attributes and edge relevance strength in real time based on an environment change triggering mechanism; according to the brightness adjustment feature set, executing hierarchical association extension calculation in a dynamic brightness adjustment index table, and dynamically determining a brightness optimization action range; and in the optimization action range, synchronously analyzing the color deviation tolerance, the power consumption constraint condition and the visual comfort index through a multi-dimensional evaluation algorithm, and generating a brightness adjustment decision parameter.

The invention relates to the technical field of camera control, and discloses a megapixel camera control method based on multifunctional fusion. The method comprises the following steps: acquiring original image data of an image sensor, and preprocessing to generate an initial brightness distribution diagram; and calculating a scene dynamic range to determine a target exposure parameter set containing exposure time, a gain coefficient and an aperture value, synchronously analyzing color temperature information, matching a preset white balance model to output a correction coefficient, and combining the two to generate a first control instruction to be sent to the image sensor. Receiving a touch screen focusing signal, and extracting a focus area coordinate to adjust a stroke parameter of a focusing motor; ambient light intensity is monitored in real time, and when the ambient light intensity is lower than a preset threshold, a flash lamp compensation module is activated and a compensation light quantity value is calculated. In the photographing mode, the output power of the flash lamp is adjusted according to the compensation light quantity value, the target exposure parameter set is recalibrated based on the focus area coordinates, and the camera complex scene adaptation and imaging quality are improved.

The invention relates to the technical field of multi-sensor identification, in particular to a coal gangue detection and classification method and system based on multi-sensor fusion discrimination, and the method comprises the following steps: obtaining visible light and infrared images, calculating brightness and intensity judgment feature conditions, executing edge detection to extract gray segments, and fusing textures and a thermal field to generate a vector set. And performing clustering analysis to finish classification judgment, and outputting a coal gangue detection classification result. According to the method, a precise trigger mechanism is established through brightness and thermal radiation double-feature screening, boundary recognition sensitivity is enhanced through gray abrupt change point division, salient region extraction capacity is enhanced through weighted fusion of texture energy and gray gradient, and a cross-modal consistency feature group is constructed through combination of two-dimensional vector construction and similarity screening. The recognition expression integrity is improved, static threshold classification is replaced by vector distribution clustering, accurate mapping and classification decision making of material attributes in a complex scene are achieved, and the stability and the recognition rate of a coal gangue detection result are guaranteed.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention relates to the technical field of vehicle-mounted display, and discloses a vehicle-mounted HUD environment illumination adaptive brightness adjusting system, which comprises an illumination acquisition module, a parameter analysis module, a model construction module, a brightness calculation module, an error evaluation module, a calibration correction module, a threshold setting module, a result output module, a feedback optimization module, a system regulation module and the like. The illumination acquisition module analyzes the illumination parameter stability and interference difference and generates an illumination perception state value; the parameter analysis module screens an optimal brightness and light intensity combination; the model construction module is used for training samples to establish a brightness and light intensity relation model; the brightness calculation module generates a brightness threshold by combining real-time illumination data mapping; the error evaluation and calibration correction module optimizes a calculation rule; and the feedback optimization module operates feedback adjustment parameters to finally generate an environment illumination adaptive brightness adjustment scheme. The system realizes accurate and dynamic adjustment of the brightness of the vehicle-mounted HUD, effectively adapts to the environment illumination change, improves the display effect and the driving safety, and has the characteristics of self-learning and self-optimization.

The invention discloses a backlight control method, display equipment and a computer readable storage medium, and relates to the technical field of liquid crystal display, the backlight control method is applied to a backlight system, the backlight system comprises an LED backlight source, the LED backlight source comprises a three-primary-color light-emitting source and a white light-emitting source, and the method comprises the steps of obtaining a to-be-displayed target image, determining a three-primary-color backlight gray value of the three-primary-color light emitting source based on the target image; a white backlight gray value is determined based on the three-primary-color backlight gray value, and the white backlight gray value is in positive correlation with the three-primary-color backlight gray value; and driving the three-primary-color light-emitting source to emit light based on the three-primary-color backlight gray value, and driving the white light-emitting source to emit light based on the white light-emitting source, so that the LED backlight source provides backlight. The light emitting efficiency of the backlight source is improved, and the brightness and the color gamut are considered on the basis of low energy consumption.

The invention relates to the technical field of size measurement, in particular to a clock part size measurement method and system based on machine vision, and the method comprises the following steps: based on clock part surface data, analyzing reflection characteristics and pixel statistics, combining a light source angle to optimize imaging, comparing gray level distribution and shadow area, and screening an image with optimal marginal definition. And fusing the brightness and contour information, screening multi-direction size data, judging the stability of boundary pixels, and outputting a size measurement result. According to the method, multiple data features are automatically collected and fused in the collection process, parameter optimization of different surface structure information is combined, cross validation of multi-direction size information is synchronously completed in the measurement link, abnormal fluctuation data are eliminated, continuous judgment and result screening are achieved in the whole processing flow, and the accuracy of data processing is improved. The stable size data of batch parts can be efficiently obtained under the automatic discrimination process, the influence of abnormal interference on the output accuracy is reduced, and the uniformity and traceability of the measurement data are improved.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention relates to the technical field of visual control, in particular to a picture defect test equipment control system and method, and the system comprises a difference tensor construction module, a space-time constraint construction module, a strain response judgment module, a coupling focusing mapping module and a boundary stability evaluation module. According to the method, the multi-dimensional difference incidence relation is constructed by extracting the pixel gray difference value, the brightness gradient and the chrominance phase angle, and fine brightness fluctuation and color shift in a picture can be captured. The superposition of the time-space change rate enables the dynamic performance of image defects to be distinguished, and continuous and transient anomalies can be effectively recognized. And in combination with tensor strain ratio and difference amplitude analysis, an abnormal region can be adaptively recognized, and neglect of a traditional method on a complex scene is avoided. The weighted aggregation of the local energy response improves the stability of the defect boundary, ensures the accurate positioning and judgment consistency of the image defect, and significantly improves the reliability and accuracy of the detection result especially in dynamic change and complex illumination environments.

The invention discloses an engineering structure crack detection method and system based on an image processing technology, and relates to the technical field of image processing, and the method comprises the following steps: S100, collecting image data of a plurality of time points in an engineering structure monitoring area, obtaining a corresponding sky brightness change curve and a reflector reflection angle change sequence, and obtaining a corresponding sky brightness change curve; generating a time-varying illumination profile covering a monitoring period and a reflector trajectory map, and constructing an illumination baseline for subsequent time sequence reference; and S200, deriving a brightness fluctuation sensitivity field according to the illumination baseline, screening an area with stable brightness response in the sensitivity field as an edge anchor point set, and carrying out initial position calibration on the crack candidate boundary by using the edge anchor point set. The crack identification stability is improved through the steps of dynamic illumination modeling, brightness sensitivity analysis, radiation calibration, boundary reestimation and the like, and high-precision, self-adaptive and sustainable optimization of crack detection in a complex environment is realized by introducing a closed-loop control and self-learning mechanism.

The invention provides an apparent defect detection system and method based on CCD image recognition. A specular reflection interference area meeting a preset difference condition is recognized through a first surface reflection image and a second surface reflection image of a target patch element; extracting corresponding orthographic gray scale data from a third surface orthographic image obtained by adopting coaxial light source irradiation based on the specular reflection interference area; according to the brightness distribution gradient of the specular reflection interference area in the first surface reflection image and the second surface reflection image, constructing a texture restoration guide map, and using the texture restoration guide map to carry out texture restoration on the orthographic gray scale data to generate a texture restoration image of the specular reflection interference area; and generating a defect identification reference image for eliminating specular reflection interference based on the texture repair image, and outputting an appearance defect identification result of the target patch element by the defect identification reference image. According to the technical scheme provided by the invention, the defect detection of the patch element can be realized under the interference of mirror reflection.

The invention belongs to the technical field of image processing, and particularly relates to an aluminum bar quality detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an aluminum bar; constructing a structure tensor for describing the distribution of the pixel points in the local gradient direction, and obtaining gradient coherence indexes of the pixel points according to feature values of the structure tensor; according to the gradient coherence index, the gray value of the pixel point and the gray value mean value and the gray value standard deviation in the neighborhood, obtaining coherence weighted saliency of the pixel point; according to the coherence weighted saliency and the coherence weighted saliency of all the pixel points in the pixel point neighborhood, acquiring a defect probability index of the pixel points; and performing aluminum bar surface quality discrimination according to the defect probability index. According to the method, through combination of gradient coherence and local brightness statistical characteristics, interference of specular reflection light spots on the surface of the aluminum bar and wiredrawing texture noise is effectively inhibited, and accuracy of detection of weak defects such as scratches is improved.

The invention discloses a high-end space light illumination control system with scene adaptability, and relates to the technical field of intelligent light control, and the high-end space light illumination control system is characterized in that spatial structure geometric information and surface material distribution are acquired, and an illumination path simulation model is constructed; in combination with a preset illumination demand template, identifying a position with illumination deviation; performing illumination coverage simulation on each position point based on the lamp type and the light emitting angle, calculating an illumination coverage contribution value, and constructing a reference matrix; according to a matrix result, lamp model selection and angle adjustment are carried out, partition control operation is executed, and dynamic optimization adjustment of the brightness, the angle and the color temperature is achieved; closed-loop correction is completed through illumination feedback; according to the invention, the adaptive ability of illumination control to a complex space structure and a dynamic scene is improved, higher control precision and energy efficiency are achieved, and the method is suitable for intelligent illumination systems in scenes such as exhibition display, commercial retail and high-end residence.

The invention belongs to the technical field of image processing, and particularly relates to a real-time image monitoring method and system for a yucca extract separation process, and the method comprises the steps: obtaining a separation process diagram, and obtaining illumination unevenness according to the brightness of pixel points; determining a self-adaptive filtering size according to the illumination unevenness, and performing enhancement processing on the image to obtain an enhanced image; obtaining a separation tendency index according to the local difference and the texture feature of the enhanced image; segmenting the image according to the separation tendency index to obtain a connected region, and obtaining a solid-phase aggregation degree according to the morphological characteristics of the connected region; and finally, judging a separation end point according to the change trend of the solid-phase aggregation degree and the regional area variance in the time window. According to the method, the problem of non-uniform illumination of an industrial site is solved through the adaptive enhancement algorithm, the separation end point is accurately judged in combination with morphological characteristics, and the monitoring accuracy and robustness are improved.

The invention relates to the technical field of screen display adjustment and visual health protection, and discloses a screen color temperature intelligent adjustment method and system, and the method comprises the steps: obtaining a user face image sequence and ambient light perception data; according to the face image sequence, eye features are extracted, a dynamic change trend is analyzed, blinking frequency and eyelid closing duration data are calculated, and a visual fatigue evaluation value is obtained; according to the ambient light sensing data and the visual fatigue evaluation value, correcting the fatigue degree and classifying fatigue stages to obtain color temperature and brightness adjustment parameters; and transmitting the adjustment parameter to a screen display driver for gradual change processing to obtain a final screen display configuration. According to the method, personalized accurate adjustment of the screen parameters is realized, visual fatigue is effectively relieved, and the use experience and the visual health protection effect are improved.

The invention relates to the technical field of image processing, in particular to an optical thin film quality detection method based on image processing, which comprises the following steps: S101, acquiring a transient light field image sequence of an optical thin film in a dynamic stress field; s102, performing spectral analysis on the brightness time sequence of each pixel point, extracting a dominant frequency component as a brightness oscillation period, processing time domain fluctuation data by adopting a Fourier transform algorithm, and calculating a local optical path difference variation based on the brightness oscillation period of each pixel point in the image sequence; and S103, projecting the phase distortion field to a preset film elastic modulus distribution model, establishing a phase distortion-stress distribution-defect mapping relation, and identifying a modulus mutation region. A dynamic stress field is generated through the synergistic effect of mechanical vibration and thermal radiation, remarkable stress concentration can be generated at the micro-defect position, the deformation difference of the micro-defect is amplified, and therefore the sensitivity of defect detection is improved.

The invention discloses an overexposure and underexposure image enhancement method, device, equipment and medium, and the method comprises the steps: enabling an image decomposition network to respectively receive a first illumination image and a second illumination image through employing two encoder-decoder networks sharing the weight, and extracting multi-scale illumination distribution information through multi-scale connection; the image reconstruction module outputs a corresponding reflectivity component and a brightness component, the image enhancement network adopts an image enhancement sub-network to adjust illumination distribution and suppress noise based on the reflectivity component and the brightness component, and the image reconstruction module multiplies the adjusted reflectivity component with an illumination image element by element and outputs an enhanced image. According to the method, the technical effects of effectively recovering image details, reducing noise interference and improving real-time performance under the overexposure or underexposure condition are reflected, the method is particularly suitable for complex environments such as power equipment monitoring, the contrast ratio of the enhanced image is better, artifacts are fewer, the calculation burden is reduced, and efficient batch processing prediction is supported.

The invention belongs to the technical field of image data processing, and particularly discloses a low-illumination image dynamic enhancement method and device, a medium and a program product, and the method comprises the steps: obtaining a to-be-processed low-illumination image, converting the to-be-processed low-illumination image into a YCbCr color space, and extracting a brightness channel of the to-be-processed low-illumination image; the brightness channel is input into an adaptive enhancement intensity parameter prediction module, the adaptive enhancement intensity parameter prediction module learns illumination features of the brightness channel through a residual block and a channel attention mechanism, and an adaptive enhancement intensity eta value is output; and inputting the original RGB image, the brightness channel and the eta value into the low-illumination image enhancement network, realizing refined enhancement through multi-scale feature fusion, and outputting an enhanced image. According to the method, the self-adaptive enhancement intensity parameter prediction module and the low-illumination image enhancement network are fused, a new low-illumination image enhancement network is formed, automatic regression estimation and de-interactive image enhancement of the self-adaptive enhancement intensity eta value are realized, and the intelligence and robustness of image brightness recovery are effectively improved.

A dimming method is to be implemented by a brightness divider, and includes steps of: (A) determining whether a brightness value is smaller than or equal to a predetermined reference value; (B) when it is determined in step (A) that the brightness value is smaller than or equal to the predetermined reference value, setting a magnitude value that is related to a current generator, and setting a pulse width value that is related to a switch based on the brightness value; and (C) when it is determined in step (A) that the brightness value is not smaller than or equal to the predetermined reference value, setting the magnitude value based on the brightness value, the predetermined reference value and a predetermined order setting, and setting the pulse width value based on the predetermined reference value.

The invention relates to a balanced color perception enhancement method for rail transit target detection, and belongs to the technical field of rail transit and computer vision. According to the method, brightness channel adaptive histogram equalization enhancement CLAHE-LC, a multi-segment tone channel mask mechanism MSHCM-M and a three-stage hybrid mechanism non-maximum suppression TSLSH-NMS technology are combined, so that the problems of complex illumination, multi-target shielding, color interference and the like in a rail transit scene are solved, and the accuracy and recall rate of target detection are improved. The method can be seamlessly integrated into an existing deep learning target detection framework, is compatible with multi-label, multi-category and multi-scale features, and remarkably improves the precision and recall rate of target detection in a rail transit scene. Experimental verification shows that the real-time performance is guaranteed, meanwhile, the detection performance is obviously improved compared with a traditional scheme, and the method is suitable for being applied to an actual rail transit safety monitoring and intelligent maintenance system.

The invention relates to the technical field of LED light source adjustment, and discloses an intelligent dynamic adjustment method and system for the brightness of an LED backlight source. The method comprises the following steps: acquiring an image frame sequence and real-time brightness; a brightness distribution histogram and inter-frame brightness variation are extracted based on the image frame sequence, image content change feature extraction is carried out, and an image monitoring result is generated; calculating target brightness and adjustment parameters of backlight partitions according to a monitoring result, performing deviation analysis and frequency amplitude weight matching in combination with real-time brightness, and generating partition adjustment demand data; smoothing the demand data to obtain a partition brightness curve and a smooth real-time index; calculating an intermediate compensation coefficient by correlating the fusion curve and the index to obtain a compensation coefficient set and a curve driving basis; judging the matching degree of the compensation demand and the amplitude weight to obtain a matching evaluation result; and finally, adjusting the brightness of the partitions, compensating inter-frame brightness variation, and determining a display output state. According to the method, the brightness adjustment precision and response speed under the dynamic content are improved.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.