106 results about "Lab color space" patented technology

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 provides a facial skin flaw enhancement method based on a Lab color space. The method comprises the following steps: firstly, acquiring an RGB face image and converting the RGB face image into a CIE Lab color space with uniform perception; then, performing differentiation treatment according to the manually selected skin flaw type: for the vascular flaw, extracting statistical characteristics of a component and driving adaptive nonlinear transformation, and generating a grey-scale map which highlights the red flaw; for pigment flaws, nonlinear transformation is carried out on the component L, and then collaborative linear weighting and feature amplification are carried out on the component L, the component a and the component b, so that a grey-scale map with highlighted pigment spots is generated. And finally, coloring the grey-scale map in the Lab color space through adjustable parameters to generate a high-contrast color enhanced image. The method overcomes the dependence on hardware and training data in the prior art, can clearly and adaptively enhance various flaws such as acnes, couperose streaks and color spots, shows robustness under different illumination, and can be widely applied to clinical beauty, later photography and real-time video processing.

The invention provides a tomato leaf health status assessment system and method, and relates to the technical field of tomato leaf health assessment, and the method specifically comprises the steps: firstly, carrying out the preprocessing of a tomato leaf image, converting the image into a Lab color space, and carrying out the segmentation and extraction of a leaf region based on a chromaticity threshold value; and then leaf area chromaticity channel statistical magnitude is counted, pixel-level anomaly detection is carried out through an adaptive threshold value, and an abnormal area mask and a normal area mask are generated. Performing connected domain analysis on the abnormal region, extracting local color and texture features, and extracting corresponding features of the normal region as a reference; the health deviation degree is obtained by calculating the feature distance between each abnormal region and the normal region, and the comprehensive health assessment index is generated by combining the area proportion of the abnormal regions after normalization. And finally, judging a health state according to a preset threshold rule and outputting an evaluation result. According to the invention, a comprehensive and accurate decision basis is provided for agricultural producers, and the automation level and evaluation accuracy of tomato disease monitoring are improved.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention discloses a fabric texture fidelity rendering method and system based on a scanned image and a target color number, and solves the technical problems of poor color perception consistency and texture detail loss. The method comprises the steps of preprocessing a standardized input image through illumination compensation, performing double-space color mapping in an LAB color space, partitioning the image by adopting K-means clustering according to texture features of a gray-level co-occurrence matrix, establishing a color mapping function containing global mapping transformation and local adaptive adjustment, and adaptively determining a fusion coefficient according to regional variance; decomposing the image into a base layer and a detail layer by adopting guide filtering, and performing spectrum constraint on the detail layer to ensure that the spectrum amplitude is consistent with the original; extracting material parameters such as a normal map, a roughness map and a metallity map; according to the method, fabric texture details are completely reserved while color perception consistency is guaranteed, rendering reality is improved, batch processing of multiple groups of target color numbers is supported, and the method is suitable for application scenes such as costume design, e-commerce display and virtual fitting.

The invention relates to the technical field of image data processing, in particular to a production speed regulation and control method and system for packaging bag printing. The method comprises the following steps: acquiring and segmenting a printed packaging bag image, and correcting rotation offset by using a histogram of oriented gradients to ensure image alignment. Then, the corrected image is converted into an LAB color space, the LAB color space is compared with a preset standard template, the ink color unevenness is calculated, and the printing quality is quantified. And finally, a relation model of the printing quality and the production speed is constructed based on historical data, the target production speed is determined, regulated and controlled, and balance of the printing efficiency and the quality is achieved. According to the invention, the influence of artificial experience dependence and image dislocation on quality evaluation is overcome, and the accuracy and stability of printing speed regulation and control are improved.

The invention relates to the technical field of batteries, in particular to a battery collector plate and a battery. The battery collector plate comprises copper and an electric connection part, and the electric connection part is used for realizing electric connection between a battery cell tab and an electrode terminal; the color parameters of the surface, used for being connected with the battery cell tab and / or the electrode terminal, of the electric connection part comprise L1 and a1, L1 is the numerical value of an L channel in the Lab color space, a1 is the numerical value of an a channel in the Lab color space, S = L1a1, and S is larger than or equal to 900 and smaller than or equal to 2500. By controlling the product S of L1 and a1 on the surface of the electric connection part to be within a proper range, light reflection can be reduced, the welding quality and stability are improved, and the structural strength of the battery collector plate is ensured.

The application discloses a colonoscopy image data enhancement method, and belongs to the technical field of computer vision, which comprises the following steps: acquiring multiple original colonoscopy images, fusing the gray information and the saturation information to obtain a set of coordinate points of the reflection area of the original colonoscopy images; calculating the set of coordinate points of the small reflection area from the set of coordinate points of the reflection area and restoring the set of coordinate points to obtain a small reflection area restoration image; converting the source image and the target domain image to the LAB color space; calculating the standard deviation and the mean value of each color channel of the source image and the target domain image except the reflection area and the dark area; migrating the color domain of the source image to the target domain corresponding to the target domain image; restoring the dark area of the migration image to obtain a color domain migration enhancement image; and composing the original colonoscopy image, the small reflection area restoration image and the color domain migration enhancement image into a colonoscopy enhancement image data set, wherein the colonoscopy enhancement image data set is used for subsequent image analysis.

The application provides an attribute recognition method, device, equipment and medium. In the application, before attribute recognition is performed based on a pre-trained attribute recognition model, for each pixel point in a first to-be-recognized image, a ratio of a first variance of each channel of the first to-be-recognized image in a LAB color space to a pre-stored standard control variance, a pre-stored control value of a standard control pixel point corresponding to the pixel point in each channel, and a first mean value of each channel of the first to-be-recognized image in the LAB color space are determined, a first corrected value of each channel of the first to-be-recognized image in the LAB color space corresponding to each pixel point is determined, a first target to-be-recognized image in a corrected RGB color space is determined based on each first corrected value, attribute recognition is performed based on the first target to-be-recognized image, and the accuracy of attribute recognition is effectively improved.

This invention provides a machine vision recognition method and system for commercially available frozen mutton slices adulterated with other meats, relating to the fields of food quality authenticity detection and computer vision technology. The invention acquires images of frozen mutton slices under standardized visible light imaging conditions, and obtains a preprocessed image set through unified resolution, normalization, circular region of interest extraction, and data augmentation. It constructs color matrix features in RGB, HSV, and Lab color spaces and generates five texture features from gray-level co-occurrence matrices, and extracts principal component features with a cumulative contribution rate exceeding 99% through principal component analysis. A random forest model is trained based on original-cut, conditioned, and reconstituted mutton slice samples to classify and identify the mutton slices to be tested. This invention achieves rapid, stable, and high-precision identification of frozen mutton slices adulterated with foreign meat and meat products under standardized illumination conditions.

The application provides a style transfer strong similarity-based generation method, device, equipment and medium, the method comprising: S10, redrawing a style reference map, outputting a clear style map; S20, inputting the clear style map into a monocular depth estimation model, outputting a single-channel depth map; S30, inputting the single-channel depth map into an enhanced image generation model for structure optimization, generating a redrawing sketch; S40, using a visual feature extraction model and a deep learning model to perform style fusion and rendering on the redrawing sketch, generating a preliminary style map; S50, pre-training a LoRA model, using the LoRA model to adjust the preliminary style map, outputting an intermediate style map; S60, after upsampling the intermediate style map, performing block optimization, outputting an optimized style map; S70, inputting the optimized style map into a Lab color space, performing histogram matching, and generating a final style map.

The embodiment of the invention provides an image processing method and device and a readable storage medium. The method comprises the following steps: acquiring a first image and a second image, wherein the second image is an image obtained by performing style editing processing on the first image; performing Lab color space conversion on the first image and the second image to obtain a first brightness channel and a first color channel of the first image and a second brightness channel and a second color channel of the second image; fusing and filtering the first brightness channel and the second brightness channel to obtain a third brightness channel; based on the pixel correlation of the first brightness channel and the second brightness channel, reconstructing the second color channel to obtain a third color channel; and outputting a fused image based on the third brightness channel and the third color channel. The fused image keeps the style of the second image and keeps the texture features of the first image, and detail distortion is avoided.

The present application relates to the technical field of thickness detection, and discloses a catenary dropper icing thickness monitoring and early warning method and system, which comprises the following steps: obtaining a to-be-detected image of a catenary dropper, converting the image to HSV and Lab color spaces, and generating a fused edge feature map; segmenting out an icing area of the dropper, and extracting a pixel width sample set; fitting the pixel width sample set, obtaining an initial icing thickness probability distribution, and extracting a measurement uncertainty factor; constructing a historical icing thickness prior distribution; determining a current measurement confidence, and calculating a fusion coefficient; performing conjugate updating on the initial icing thickness probability distribution and the historical icing thickness prior distribution, obtaining an icing thickness posterior probability distribution and a distribution variance; obtaining a real-time environmental wind speed, calculating a safety thickness threshold and an early warning probability threshold; and when the cumulative probability of the icing thickness posterior probability distribution in the part exceeding the safety thickness threshold is greater than the adjusted early warning probability threshold, outputting an early warning signal. The present application improves the accuracy of the icing thickness monitoring result.

The application provides a cloud image efficient synthesis method, belonging to the field of image processing, comprising the following steps: calculating the edge distance transform of the mask of the foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image to the Lab color space, and inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network to output a preliminary fusion image in the Lab color space; using a brightness discriminator and a chroma discriminator to respectively judge the local texture consistency of the preliminary fusion image and a real fusion image in the L channel, and the global color harmony of the preliminary fusion image and the real fusion image in the a and b channels; performing the adversarial training of the generator network and the discriminator based on a total loss function until the network model converges, and using the converged generator network to generate a final fusion image. The application can guarantee the high realism and consistency of the fusion image in the details and the overall color tone.

The application discloses a bubble detection, tracking and parameter quantification method and device based on an improved YOLOv8. The method comprises the following steps: collecting continuous frame visible light RGB bubble images; converting the RGB bubble images into LAB color space, performing adaptive enhancement on an L channel, performing color balance on A and B channels, and obtaining a pretreatment image after standardization and normalization processing; inputting the pretreatment image into an improved YOLOv8 bubble detection model, setting a multi-scale feature enhancement module after a P4 layer of the model, and adopting a bidirectional feature fusion structure and a decoupling detection head to output a bubble detection result; performing cross-frame association based on continuous frame detection results to obtain a bubble trajectory sequence; and calculating bubble aspect ratio and motion speed parameters according to a detection bounding box and the trajectory sequence. The application is suitable for bubble detection, tracking and parameter quantification analysis in a complex background.

The invention discloses an image super-resolution reconstruction method and system, and belongs to the field of digital cultural heritage protection, and the method comprises the steps: constructing a multi-resolution image pyramid, carrying out the semantic analysis based on a low-resolution layer, recognizing a key structure and a uniform region, and generating a self-adaptive dicing strategy; dynamically selecting a super-resolution model in combination with texture complexity grading through streaming loading of the blocks and the overlapping bands to a GPU (Graphic Processing Unit); and executing global color micro-correction in a Lab color space, and generating a seamless high-definition image by applying Poisson fusion in an overlapping zone region. According to the method, boundary artifacts, texture fractures and color deviations caused by blocking processing can be eliminated, video memory occupation is reduced, and calculation efficiency is optimized.

This invention relates to a urine test strip color recognition detection method based on image analysis, comprising the following steps: Step 1, acquiring a color image; Step 2, detecting black calibration blocks at the four corners; Step 3, geometric correction of the test strip area based on four-point perspective transformation; Step 4, adaptive extraction of the effective area of ​​the test strip; Step 5, precise segmentation of color blocks; Step 6, correction of uneven illumination and color standardization; Step 7, multidimensional similarity calculation based on weighted Lab color space; Step 8, outputting the detection results and marking abnormalities. The beneficial effects of this invention are: the positioning accuracy remains ≥99.8% even in complex scenarios, far exceeding existing template matching and QR code solutions; it is completely unaffected by test strip deformation or printing offset; the CV value of the same urine sample repeated under different mobile phones and different lighting conditions is <3%; it successfully solves the problems of batch-to-batch color difference and test strip aging, with a clinical concordance rate ≥98%; the entire algorithm has a small computational load, fully meeting the needs of real-time detection.

The invention provides an efficient cloud image synthesis method, which belongs to the field of image processing and comprises the following steps of: calculating edge distance transformation for a mask of a foreground image to generate a multi-level boundary information graph; converting the background image and the foreground image into a Lab color space, inputting the Lab channel, the mask and the multi-level boundary information graph into a generator network together, and outputting a preliminary fusion image in the Lab color space; a brightness discriminator and a chrominance discriminator are adopted to discriminate the local texture consistency of the preliminary fusion image and the real fusion image in the L channel and the global color harmony of the preliminary fusion image and the real fusion image in the a channel and the b channel respectively; and performing adversarial training on the generator network and the discriminator based on the total loss function until the network model converges, and generating a final fusion image by using the converged generator network. According to the method, the high reality sense and consistency of the fused image in detail texture and overall hue can be ensured.

This invention relates to the field of food safety testing technology and discloses an automatic matching method for batch parameters of malathion test cards with QR code traceability. The method includes collecting the dual-matrix Lab color space values ​​of the QR code label paper and NC film in the test card, constructing an aging decay function relationship table and an initial coupling correlation matrix, and encoding an encrypted QR code. During testing, a single frame of original image is acquired to extract real-time chromaticity feature vectors of the dual-matrix region, and the aging feature modulus is calculated by combining it with the initial coupling correlation matrix. Based on the aging feature modulus, the aging decay function relationship table is queried to dynamically correct the initial standard curve parameters and generate a real-time standard curve, thereby outputting the malathion concentration. This invention constructs an intrinsic aging indication mechanism through dual-matrix chromaticity difference, achieving adaptive reconstruction of the standard curve according to the aging state of the test card, ensuring detection accuracy throughout the entire lifecycle.

The invention provides a control device, a control method and a control program capable of performing color adjustment among a plurality of projectors in a short time. The control device includes a processor (51) that instructs the first projection device (10a) to project in a first color that is an adjusted color, acquires a color measurement value of light projected from the first projection device (10a) (XYZ first color PJ1), instructs the second projection device (10b) to project in the first color, acquires a color measurement value of light projected from the second projection device (10b) (XYZ first color PJ2), and acquires a color measurement value of light projected from the second projection device (10b). On the basis of a lookup table (62) for a first color indicating the relationship between the color adjustment parameter (P) and the position in the Lab color space (61), a color measurement value (XYZ first color PJ1) of the first projection device (10a), and a color measurement value (XYZ first color PJ2) of the second projection device (10b), a color adjustment parameter (Ppred) is determined such that the position of the color measurement value (XYZ first color PJ1) in the Lab color space (61) approaches the position of the color measurement value (XYZ first color PJ2) in the Lab color space (61).

The application provides a text color matching method and device, electronic equipment and storage medium. The method comprises: obtaining a background picture for displaying a target text to be matched, and position information of the target text in the background picture; obtaining color information of the background picture in an HSV color space and a Lab color space; obtaining a color saliency map of the background picture relative to a region where the target text is located based on the color information and a constraint condition of text color matching, and obtaining at least one alternative text color in the color saliency map; wherein the constraint condition comprises at least one of a brightness constraint condition, a hue constraint condition, a saturation constraint condition and a lightness constraint condition, and the constraint condition is obtained based on the color information; and obtaining a final text color of the target text from the alternative text color. Thus, the beneficial effects of not needing to manually set the alternative color of the text in advance, improving the readability of the text color matching, and improving the matching degree with the background color are achieved.

The invention relates to the technical field of thickness detection, and discloses a catenary dropper icing thickness monitoring and early warning method and system, and the method comprises the steps: obtaining a to-be-detected image of a catenary dropper, converting the to-be-detected image to an HSV and Lab color space, and generating a fusion edge feature map; segmenting a dropper icing area, and extracting a pixel width sample set; fitting the pixel width sample set to obtain initial icing thickness probability distribution, and extracting a measurement uncertainty factor; historical icing thickness prior distribution is constructed; determining a current measurement confidence coefficient, and calculating a fusion coefficient; performing conjugate updating on the initial icing thickness probability distribution and the historical icing thickness prior distribution to obtain icing thickness posterior probability distribution and distribution variance; acquiring a real-time environment wind speed, and calculating a safety thickness threshold value and an early warning probability threshold value; and when the cumulative probability of the icing thickness posterior probability distribution exceeding the safety thickness threshold is greater than the adjusted early warning probability threshold, outputting an early warning signal. According to the invention, the precision of icing thickness monitoring results is improved.

The application relates to the technical field of light-emitting materials, and discloses an in-vitro multiplex detection method and related equipment. The in-vitro multiplex detection method comprises the following steps: obtaining a first photoluminescence spectrum corresponding to a plurality of up-conversion luminescence coding materials and a second photoluminescence spectrum corresponding to a magnetic separation product, obtaining a color difference between the second photoluminescence spectrum and each first photoluminescence spectrum in a LAB color space, and comparing each color difference with a preset threshold value, so that the target type of a sample to be detected can be determined. The first photoluminescence spectra do not need to have different luminescence peak wavelengths, and obvious photoluminescence color differences do not need to be generated. In the multiplex detection application, the type of the up-conversion luminescence coding materials that can be used is greatly increased, and errors caused by human eye judgment and equipment limitations can be effectively avoided, so that the accuracy of the in-vitro multiplex detection is improved.

The invention discloses an intelligent monitoring and recording system for wound healing after an orthopedics department, and belongs to the technical field of medical image processing, the system comprises a multi-modal feature extraction module, a space-time coupling healing analysis module, a self-adaptive threshold optimization module and an intelligent record generation module, the multi-modal feature extraction module fuses RGB color, Lab color space and texture features; the space-time coupling healing analysis module establishes a time-space two-dimensional coupling model of the wound and generates a healing rate parameter and a healing stage identifier, and the adaptive threshold optimization module dynamically generates an anomaly detection threshold according to historical data and reversely adjusts the weight of the space-time coupling model to form closed-loop optimization, so that the healing speed of the wound is improved. The intelligent record generation module automatically generates a standardized electronic record, accurate evaluation, dynamic tracking and intelligent recording of the orthopedic postoperative wound are achieved, and the nursing quality and efficiency are remarkably improved.

The invention discloses an asphalt pavement patching area detection and color matching analysis method and system, and belongs to the technical field of road maintenance engineering intelligent detection, and the method comprises the steps: recognizing a patching area through a YOLO target detection model, and outputting bounding box coordinates; adopting a K-Means clustering algorithm to extract representative RGB color values from the repairing area and the original pavement area; the RGB color value is converted into a Lab color space, and a color difference delta E value is calculated based on a CIE color difference formula; the system comprises an image acquisition module, a target detection module, a color clustering analysis module, a color difference calculation module, a data storage module and a visual interaction module, and realizes integrated processing of repair area detection and color matching evaluation. And quantitative data support is provided for pavement patching quality evaluation.

The invention discloses a food mildew detection method and system based on visual identification, and the method comprises the following operation steps: collecting a high-resolution color image of a to-be-detected food, carrying out the preprocessing, and converting the high-resolution color image into a Lab color space, thereby obtaining a to-be-detected image; filtering the to-be-detected image to obtain an enhanced brightness image; extracting a filtering response value from the brightness-enhanced image to calculate a flocculent texture expression degree; edge intensity and an H component are extracted from the to-be-detected image; determining candidate seed points by using the flocculent texture representation degree, the edge strength and the H component; determining the growth direction consistency by using the candidate seed points to obtain a mildewed area; and carrying out mould analysis on the mould area, and dividing mould categories. Whether the mould exists in the food or not can be judged firstly by identifying the mould area, so that the type of the mould is identified, and the food can be accurately detected.

This invention relates to the field of machine vision technology, specifically to a machine vision-based online defect detection method for printed materials. The method includes the following steps: employing a multispectral LED array light source to enhance the contrast of printed surface features by dynamically adjusting the spectral distribution; acquiring high-resolution images using a linear CCD camera; achieving rapid registration of the image with a standard template using an improved SIFT-Net algorithm to address deformation issues during high-speed motion; extracting luminance and chromaticity components from the Lab color space; removing noise using nonlocal mean filtering; predicting equipment wear trends using an LSTM model; and feeding the analysis results back to the production module to obtain an online defect detection solution. This invention solves the problems of poor contrast in traditional printed material detection light sources, insufficient feedback between detection results and the printing press, and the inability to deeply mine defect data to optimize processes and predict equipment wear.

The application discloses a flexible LED filament on-power detection method and system based on image processing, which comprises the following steps: collecting the RGB images of the light-emitting points of a plurality of flexible LED filaments after power-on, converting the RGB images to Lab color space, and extracting the L channel image and the ab channel image respectively; pre-processing the L channel image to obtain a binary mask image; determining the connected domain according to the binary mask image to obtain the brightness area, screening out the qualified segmented connected domain as a concentric annular region, eliminating the LED filaments exceeding the preset change rate according to the gradient change rate, calculating the Mahalanobis distance of the chromaticity coordinates and the standard value based on the ab channel image of the remaining flexible LED filaments, eliminating the corresponding flexible LED filament when the Mahalanobis distance exceeds the distance tolerance, and marking the finally remaining flexible LED filament as a qualified product. The application realizes comprehensive evaluation of the brightness uniformity and chromaticity consistency of the flexible LED filament, and takes into account the detection efficiency and precision.

The present application relates to the field of machine vision and optical detection technology, 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 tea samples to obtain original data with spectral and structural characteristics; separating the specular and diffuse reflection components of the tea surface and performing spectral consistency correction combined with the radiation baseline; performing multi-band spectral reconstruction and normalization processing based on the corrected diffuse reflection image, mapping to the CIE Lab color space, extracting pixel-level brightness, chrominance and color distribution statistical indicators, combining spatial weighting matrix optimization to generate color feature vectors; constructing a quantitative evaluation and grade determination model, calculating the final score by combining the perceived color fraction, dry calibration color fraction and penalty term, and outputting the grade result. The present application realizes objective, accurate and automatic evaluation of tea color.

The invention discloses an objective index method for evaluating a dichroism contrast improvement condition. The method comprises the following steps: converting a normal image and a first image represented by a red-green-blue color space into a first conversion image and a second conversion image represented by a laboratory color space; obtaining an initial pixel in the first conversion image; respectively calculating the difference values of the brightness component, the red-green component and the yellow-blue component in the eyes of the person with normal color vision; target pixels which are difficult to distinguish are determined in the initial pixels according to the difference value; respectively calculating a plurality of experimental color differences in combination with the weight adjustment coefficient of the lightness difference; the plurality of experimental chromatic aberration are chromatic aberration of the target pixel in the eyes of the abnormal color vision person and the normal color vision person in the first conversion image and chromatic aberration of the target pixel in the eyes of the abnormal color vision person in the second conversion image, an objective index value corresponding to the first image is calculated in combination with a chromatic aberration value adjustment coefficient, and then an optimal image is screened out.