75 results about "Gaussian pyramid" patented technology

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses a deep learning-based mixed-shooting image correction method and system, and relates to the field of computer vision, and the method comprises the steps: carrying out the illumination normalization processing of a mixed-shooting image group, and constructing a Gaussian pyramid image group; inputting the Gaussian pyramid image group into a ResNet-34 network to extract a multi-scale feature map, and generating spatial position and channel statistical information; and converting the spatial position and the channel statistical information into a DNA sequence fragment through a DNA base mapping rule. Illumination normalization is achieved through Retinex decomposition and Gaussian filtering, the exposure difference of different devices is effectively eliminated, the input quality of follow-up processing is guaranteed, traditional feature description is converted into biological sequence comparison through DNA base coding, geometric parameters are optimized in combination with quantum annealing, and the accuracy of the method is improved. The matching precision of a low-texture area and the correction effect of a large-view-angle-difference scene are remarkably improved, and the sub-pixel-level matching precision is achieved based on weighted RANSAC and multi-scale pyramid fusion.

The invention relates to the technical field of computer vision and robot positioning, and discloses a dynamic 3DGS-SLAM method and system based on Gaussian pyramid and adaptive densification. The method comprises the following steps: generating a semantic mask through real-time dynamic target detection so as to distinguish a static background from a dynamic object; constructing a 3D Gaussian map model of the scene; rendering images in parallel on a full-resolution view and a down-sampling resolution view by adopting a motion perception Gaussian pyramid rendering method, and fusing rendering results by utilizing a dynamic mask so as to eliminate motion edge artifacts; a self-adaptive densification strategy based on a monotonic attenuation function is adopted, the initialization radius of the Gaussian ellipsoid is dynamically adjusted based on the training progress, and high-density initialization and progressive redundancy pruning are executed to optimize the geometric fidelity; and finally, performing coarse-to-fine camera pose optimization, and outputting a precise pose and a high-fidelity static map. According to the method, the problems of tracking drift and rendering artifacts under dynamic interference are effectively solved, and the positioning precision and the rendering quality are improved.

The invention belongs to laser stripe image data processing, and particularly relates to a multi-scale gradient gravity center laser center line extraction method and system, and the method comprises the steps: S1, carrying out the feature extraction and enhancement of a laser stripe image, intensifying detail branches through a space channel weight map, obtaining a bounding box positioning result based on depth separable convolution and residual connection, and obtaining a bounding box positioning result; s2, acquiring a prediction center for each column by adopting a prediction method, and searching a gray peak value in a normal adaptive window to realize coarse positioning; the method comprises the following steps: S1, building a local coordinate system and an elliptical window along the coarse center line, and constructing a multi-scale Gaussian pyramid to carry out background suppression and image enhancement, S4, generating a weighted graph through cross-scale fusion, calculating a weighted gray gravity center in each window, and carrying out image enhancement on the weighted graph. And after smoothing, a high-precision laser stripe center line is output. And the accuracy and robustness of extracting the laser center line are improved.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.

The invention relates to the technical field of digital image processing, in particular to a real-time image quality enhancement method for a vehicle-mounted camera EMC test, and the method comprises the steps: obtaining real-time image data and historical image data; and performing multi-branch collaborative decoupling processing on the real-time image data to generate a frequency domain purification graph, a core structure graph, a space domain purification graph and a time domain stability graph. And performing local statistical analysis on the real-time image data to generate an artifact intensity map and a perception saliency map. Under a multi-resolution Laplacian pyramid framework, fusing the frequency domain purification graph and the space domain purification graph; the weight of the fusion process is intelligently regulated and controlled spatially and hierarchically by an artifact intensity graph, a perception saliency graph and a time domain stabilization graph which are constructed based on a Gaussian pyramid, and finally a high-quality enhanced image is reconstructed and generated. According to the invention, through a multi-branch cooperative decoupling and intelligent fusion method, real-time enhancement of the vehicle-mounted camera image under EMC interference is realized.

The invention discloses a depression detection method fusing a multi-modal attention mechanism, and relates to the technical field of auxiliary psychological health diagnosis, and the method comprises the steps: respectively extracting a facial image and a voice signal from an original video, extracting a global feature map from the facial image through a deep convolutional network, and introducing a local fusion module and a global fusion module; combining a Gaussian pyramid attention module with a spatial domain attention module; speech signals are converted into a Mel spectrogram through preprocessing, speech features are extracted through a SincNet network, and the speech features are further sent to a time-frequency attention module to highlight depression-related intonation and energy changes; in the feature fusion stage, a self-adaptive attention mechanism is adopted, self-attention modeling is performed on face and voice modes, cross-mode fusion and context modeling are completed through a Query-Key-Value structure, and a depression score is output through a full connection layer. According to the scheme, the problem that cross-modal nonlinear correlation is difficult to dynamically capture is solved, and effective technical support is provided for mental health assessment and intervention.

The invention relates to a riverway ice condition identification method based on optics-SAR fusion and adaptive segmentation, and belongs to the technical field of remote sensing image processing and application. Riverway ice condition features are extracted from the optical remote sensing image, and a Ka-SAR feature map is extracted from the Ka-SAR image by adopting an improved high-resolution network; carrying out multi-modal and multi-scale feature fusion on the extracted features, carrying out scale specificity feature extraction by adopting a Gaussian pyramid, and then carrying out weighted fusion on the river ice condition features and the Ka-SAR features on each scale based on a scale specificity weight distribution principle; aggregating the multi-scale fusion features into a final fusion feature map by adopting a bottom-up pyramid reconstruction strategy; improved Kuan filtering is used to optimize the fused feature map, and adaptive threshold segmentation is used to realize ice surface and non-ice surface binary classification in the feature map. The method can achieve the precise segmentation of the ice condition region, and improves the recognition precision of the thin ice region.

This application discloses a real-time image stitching method and apparatus. The method includes: receiving images uploaded to the cloud by a camera device in real time; extracting feature points from the images and matching feature points between the current frame and the previous frame; calculating the ratio of the homography matrix score to the sum of the homography matrix score and the fundamental matrix score to determine whether to use the homography matrix or the fundamental matrix to recover the image's pose parameters and map points, and initializing the map; estimating the pose of the current frame and optimizing the pose of the current frame using candidate frames; converting the coordinates of the map points to two-dimensional coordinates and performing plane fitting; converting the coordinates of the image corner points in the camera coordinate system to the object coordinate system, calculating perspective transformation parameters and performing geometric transformation on the image; and performing Gaussian pyramid fusion on the generated tiles. This method solves the problems of low efficiency in scene reconstruction using motion reconstruction algorithms and the inability to process real-time photogrammetric data.

This invention relates to an image denoising method, system, and readable storage medium. The method includes: performing Gaussian filtering and downsampling on a current frame image to obtain a first downsampled image; performing Gaussian pyramid decomposition to obtain a current frame image group; upsampling to obtain an upsampled image, and subtracting it from the current frame image to obtain high-frequency information; performing motion estimation and texture estimation at different scales on the current frame image group and a reference frame image group, and correcting them with guided filtering; performing spatiotemporal filtering on the first downsampled image and the reference frame image based on motion estimation weights; upsampling the denoised downsampled image to obtain a denoised upsampled image; and weighted fusing the denoised upsampled image and high-frequency information according to texture estimation weights to obtain a final image; downsampling the final image and performing Gaussian pyramid decomposition to obtain a reference frame image group for denoising the next frame image. This invention can preserve more texture details while increasing the image signal-to-noise ratio.

The application discloses an environment monitoring method and device, which is used for realizing accurate counting of river floating objects in a river monitoring image, so that a more accurate and real-time monitoring result of the river environment quality is obtained. The environment monitoring method provided by the application comprises the following steps: determining a river monitoring image; performing Gaussian filtering and smoothing processing on the river monitoring image, and performing down-sampling on the image obtained through the Gaussian filtering and smoothing processing for a preset number of times; constructing a Gaussian pyramid model by using the image obtained through the Gaussian filtering and smoothing processing and the image obtained through each down-sampling; the Gaussian pyramid model comprises multiple layers of images, wherein the bottom layer of image is the image obtained through the Gaussian filtering and smoothing processing, and each layer of image other than the bottom layer is the image obtained through one down-sampling; determining a region of interest of the river monitoring image by using each layer of image of the Gaussian pyramid model; counting the river floating objects in the region of interest; and determining the river environment quality based on the counting result.

This invention discloses a near-infrared image dehazing method based on bright area clustering optimization. The method is characterized by first acquiring a hazy near-infrared image, then constructing an image dehazing model and building a Gaussian pyramid for the hazy near-infrared image. Candidate bright area binary masks are extracted and refined at each scale, and after mapping and fusion, a baseline candidate bright area binary mask and scale persistence features are obtained. Next, feature extraction and cluster analysis are performed on the baseline candidate bright area binary mask, and a physical consistency cost function is constructed based on an atmospheric scattering model for verification, obtaining a global atmospheric light estimate. Then, the global atmospheric light estimate is used for normalization and adaptive dark channel extraction and fusion to obtain an initial transmittance distribution, which is then refined and constrained to obtain an optimized transmittance distribution. Finally, the dehazed image is reconstructed based on the atmospheric scattering model. The advantages are improved transmittance estimation accuracy and edge preservation capability, achieving high-quality restoration of hazy near-infrared images.

The invention relates to the technical field of medical image processing, and discloses a chronic obstructive pulmonary emphysema distribution quantitative method and system based on multi-scale segmentation, and the method comprises the steps: carrying out the anisotropic diffusion filtering noise reduction of a chest CT image; segmenting a lung field and removing a blood vessel bronchial structure by adopting a region growing algorithm; multi-scale image representation is constructed based on a Gaussian pyramid, an emphysema candidate area is identified in a coarse scale layer, and a boundary is accurately drawn by adopting a self-adaptive threshold value in a fine scale layer; extracting local texture features to distinguish the lobular central emphysema and the total lobular emphysema; dividing severity levels according to spatial aggregation characteristics and density gradient distribution, and calculating an air swelling volume ratio and a distribution heterogeneity index; the three-dimensional pseudo-color volume is used for drawing visualization, a structured quantitative report is generated, accurate segmentation and subtype classification of the emphysema area are achieved, and comprehensive quantitative analysis indexes are provided.

The invention discloses an SIFT (Scale Invariant Feature Transform) algorithm hardware circuit implementation, which optimizes the hardware circuit implementation of an original SIFT algorithm, and solves the problems of slow operation of the SIFT algorithm and large resource consumption when the SIFT algorithm is deployed on an FPGA (Field Programmable Gate Array). According to the specific implementation scheme, the method comprises the steps that a one-layer six-group Gaussian pyramid is built, and an independent precalculated 21 * 21 Gaussian filtering kernel is used in each group; extreme points of three adjacent groups of differential pyramids are detected by adopting a threshold method, and edge response points are eliminated; a CORDIC algorithm is adopted to calculate the gradient direction and amplitude of a Gaussian image where the feature points are located, and a 16-row 16-column calculation result is output for gradient histogram statistics and descriptor generation; and normalization of a 128-dimensional descriptor is realized by using a single divider. The method has the advantages of low resource consumption, high real-time performance and the like, and is suitable for scenes, such as an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit) and the like, needing hardware acceleration of the SIFT algorithm.

The invention discloses an image definition assessment method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a to-be-assessed grayscale image; performing Gaussian pyramid downsampling processing of the target layer number on the to-be-evaluated grayscale image to obtain a to-be-evaluated multi-scale image corresponding to the target layer number; based on a dynamic gradient threshold denoising method, carrying out definition score calculation on the to-be-evaluated grayscale image and each to-be-evaluated multi-scale image to obtain an image definition score corresponding to the to-be-evaluated original image and each to-be-evaluated multi-scale image; and determining target image definition corresponding to the to-be-evaluated original image based on the to-be-evaluated original image and the image definition scores corresponding to the to-be-evaluated multi-scale images. According to the method, comprehensive evaluation of the image definition is realized by analyzing the multi-scale features of the image and performing dynamic threshold processing, meanwhile, noise is effectively suppressed, important details such as image edges and the like are reserved, and the accuracy and robustness of image definition evaluation are improved.

The present application relates to a kind of microbial microscopic image target feature identification method, device and storage medium, applied to image processing technical field, comprising: by marking the image on the target to be identified, convert the target to be identified into different scale space and construct multiple groups of Gaussian pyramid and convert into feature pyramid, according to feature pyramid, obtain feature point, obtain the feature parameter of each feature point, when the target needs to be identified, convert the image to be identified into multiple groups of Gaussian pyramid, and the pixel point in Gaussian pyramid is matched with feature point, if the number of pixel point on the image to be identified is matched exceeds the number of pre-set, then consider that there is identification target on the image;By the present application, without using convolutional neural network, microorganism image features can be quickly and accurately automatically identified, and then automatically identify white blood cells, clue cells, spores, blastospores, hyphae, trichomonas and other microorganisms.

A method and device for decoding an encoded data is based on a hybrid implicit neural representation (INR) network comprising a plurality of INR networks arranged in a plurality of hierarchic levels and latent variables being arranged in a plurality of hierarchic levels. The ground truth images are decomposed using Gaussian pyramid to generate the output images at different resolutions, making it possible to learn one synthesis network per resolution for example. The reconstruction process may be performed in a coarse to fine manner, i.e. from the lowest to the highest resolution.

The invention is suitable for the technical field of falling object detection, and provides a high-altitude falling object detection method and system based on machine vision, and the method comprises the following steps: carrying out the collection of an original image, carrying out the Gaussian pyramid decomposition of the original image, obtaining a multi-layer image, calculating the stability cardinal number of each layer of image, and determining a reference layer image based on the stability cardinal number; establishing a background model for the reference layer image, and performing weighted fusion on the non-reference layer image after down-sampling; extracting a current frame image, performing difference calculation on the current frame image and the background model to obtain a difference graph, and determining an adaptive threshold value; and carrying out binarization on the difference graph based on an adaptive threshold value, generating a foreground mask, and carrying out target detection and track initialization based on the foreground mask. According to the method, the appropriate reference layer can be automatically selected according to the stability of the images in different scenes, the influence of interference factors such as leaf shaking and sudden illumination change on background modeling is effectively reduced, the accuracy of high-altitude falling object target detection is improved, and the false detection rate is reduced.

The application discloses a kind of video vibration two-dimensional modal identification method and equipment based on depth completion pyramid, belong to structural vibration measurement and modal identification technical field;First, the depth image sequence in the two-dimensional vibration process of measured structure is obtained;The sparse depth image of depth image sequence is directionally depth completed to construct Gaussian pyramid, and depth completion pyramid is constructed in combination with the Gaussian pyramid of adjacent scale, and the target layer is selected for fusion reconstruction;Then, the reconstructed depth sequence is sequentially subjected to difference amplification, transposition compression and direct current removal processing to construct a unified input matrix;Then, the input matrix is subjected to PDD method and H-DMD method to extract natural frequency, damping ratio and modal shape from two dimensions of frequency domain and time domain;Finally, the decomposition results of PDD method and H-DMD method are jointly checked, and the modal identification result of the structure is output.The two-dimensional structural vibration modal result obtained by the method is improved in terms of parameter integrity, spatial continuity and physical consistency.

The invention discloses an efficient real-time construction method and system for a large-range environment consistency three-dimensional map. According to the method, the global map deviation is controlled within a reasonable range through fusion pose estimation of the multi-source constraint improved DPVO and the wheel type odometer, a priori anchor point is used for assisting closed-loop triggering optimization, high-weight absolute anchor point constraint is set, a Gaussian pyramid is constructed in a self-adaptive manner, high-precision positioning is realized in a hazardous chemical substance warehouse, and the positioning accuracy is improved. The robot is effectively prevented from colliding with the goods shelf. According to the method, double-thread parallel mapping is adopted, so that the output frame rate of the local map meets the requirement, the conventional moving speed of the robot can be matched, and no lag of navigation is ensured. According to the invention, through multi-dimensional mask subgraph management and an adaptive pyramid, continuous inspection of the warehouse is realized, and full-process data support of'mapping-positioning-alarming-decision 'is provided for safety supervision of the hazardous chemical substance warehouse.

The application discloses a U-Net-based optical and SAR remote sensing image optical flow registration method and relates to the technical field of heterogeneous image registration, and comprises the construction of an image data set, image preprocessing and the labeling of a region of interest; two U-Net network models are used to train an optical remote sensing image segmentation model and a SAR remote sensing image segmentation model respectively; U-Net network segmentation results of a to-be-registered image pair are acquired, and pixel point sets marked in specified channels of two segmentation images are recorded respectively; gradient operators are used to construct class GLOH descriptors of region-of-interest feature points and pixel points in specified neighborhoods of the region-of-interest feature points in the to-be-registered image pair; and the region-of-interest feature points in the to-be-registered image pair are registered by using a Gaussian pyramid LK optical flow method. The application can realize the registration of heterogeneous images with higher precision, stronger purpose and better timeliness.

The present invention relates to a defogging method based on the combination of stochastic differential equations and Gaussian pyramids. High-resolution and low-quality foggy images are collected and input into a Gaussian pyramid network for decomposition to generate a first multi-layer feature map and a second multi-layer feature map. The first multi-layer feature map is subjected to a diffusion process to generate a noise feature map. The second multi-layer feature map and the noise feature map are input into a NAFNet network for training to predict noise. The noise feature map is input into the NAFNet network and, through iterative processing, an initial state noise-free feature map is generated. The initial state noise-free feature map is input into a Gaussian pyramid network and, through layer-by-layer reconstruction, a defogging image is obtained. The present invention relates to a defogging method based on the combination of stochastic differential equations and Gaussian pyramids. High-resolution and low-quality foggy images are input into the Gaussian pyramid, and Fourier transform is introduced into the noise prediction network NAFNet. A loss function is introduced to optimize model parameters to improve the defogging effect of the image.

The application relates to an infrared and visible light image fusion method based on multi-scale decomposition and partial differential equations, and belongs to the technical field of image fusion. The method solves the problems of blurred target edges and details in a current fusion image and large resource consumption. The method comprises the following steps: obtaining an infrared image and a visible light image including a target object through synchronous acquisition, and carrying out pretreatment and adaptive multi-scale decomposition; the number of layers of a Gaussian pyramid is obtained based on the complexity of the visible light image and the infrared image; the pretreated visible light image and the infrared image are subjected to multi-scale decomposition by using the Gaussian pyramid corresponding to the number of layers; each decomposition image is subjected to smoothing filtering and detail enhancement to obtain an enhanced image of each scale; the enhanced images of the infrared and visible light of the same scale are subjected to dynamic fusion, and all the scale images obtained through the fusion are reconstructed to obtain a final fusion image. The method realizes the reservation of target edges and details in the fusion image by using small resources.

The present invention discloses an adaptive crack identification and quantification method around an open-pit mine, including image preprocessing and crack identification steps. In view of the complexity of the open-pit mine environment, the present invention adopts a Gaussian pyramid to perform multi-scale analysis on the image, and realizes grayscale equalization through gamma transformation to adapt to the crack identification needs under different lighting conditions; in the crack identification part, the present invention uses an improved difference entropy to optimize the local threshold segmentation algorithm, aiming to achieve high-efficiency and high-precision detection of mine cracks. This method can dynamically determine the segmentation threshold according to the grayscale distribution of the local area of ​​the image, effectively improving the accuracy and robustness of crack identification; the method of the present invention not only greatly improves the automation level of crack detection, but also provides strong technical support for the safety management and risk warning of open-pit mines, and has important practical application value and broad market prospects.

The invention discloses a similar image search method, system and device based on perceptual hash and a medium thereof, and the method comprises the steps: constructing a multi-scale space for an input initial image, generating a Gaussian pyramid, and obtaining a Gaussian difference pyramid based on the Gaussian pyramid; screening stable key points in the Gaussian difference pyramid; according to the distribution density of the screened stable key points, delimiting a densest area in the initial image, and intercepting a minimum bounding rectangle as a target screenshot image; extracting multi-modal features of the initial image, and performing feature splicing to generate a joint feature vector; preprocessing the initial image and the target screenshot image; generating a corresponding Hash character string based on the preprocessed image and the joint feature vector; and comparing the Hash character string of the image to be retrieved with the Hash set in the database, and counting the image of which the Hamming distance is smaller than or equal to a preset threshold value as a similar result to be output. According to the method, the search accuracy of the screenshot image is improved, so that complete original image information is obtained.

The application discloses a similar image search method, system and device based on perceptual hashing and a medium thereof. The method comprises constructing a multi-scale space for an input initial image and generating a Gaussian pyramid based on the multi-scale space, obtaining a Gaussian difference pyramid based on the Gaussian pyramid, screening stable key points in the Gaussian difference pyramid, delimiting a densest region in the initial image and intercepting a minimum circumscribed rectangle as a target screenshot image according to the distribution density of the screened stable key points, extracting multi-modal features of the initial image, performing feature splicing to generate a joint feature vector, preprocessing the initial image and the target screenshot image, generating corresponding hash strings based on the preprocessed images and the joint feature vector, comparing hash strings of images to be searched with a hash set in a database, and outputting images with a hamming distance less than or equal to a preset threshold as similar results. The application improves the search accuracy of screenshot images to obtain complete original image information.

Embodiments of the present application relate to a video saliency map generation method, device and storage medium. The method comprises: constructing a plurality of Gaussian pyramids containing different scales for each preset feature channel according to a plurality of preset feature channel information of a video frame; wherein the preset feature channels include brightness, color and edge; determining a first saliency map for each preset feature channel according to the Gaussian pyramids of a plurality of preset feature channels; obtaining simulated optical flow by calculating motion information under a preset dimension through inter-frame difference after different level image displacement according to brightness information of a plurality of video frames; extracting a second saliency map of a preset direction and a preset speed based on a center-edge difference method according to optical flow-speed information; and determining a video saliency map according to a static saliency map synthesized according to the first saliency map and a dynamic saliency map synthesized according to the second saliency map. The technical solution of the embodiments of the present application has fast saliency map calculation speed, small calculation overhead, good effect and strong interpretability.

The invention discloses a splicing algorithm dense feature point matching management tool based on GIMP, and the tool comprises a dense feature point detection matching algorithm: constructing a Gaussian pyramid for a selected matching image on a plurality of scales, and enabling each scale to correspond to a different resolution; acquiring a specified matching area of the matching image, and performing grid setting under different resolutions of the matching area; in each divided grid region, calculating the gradient intensity and direction of the image, and performing histogram statistics according to the gradient direction to form a direction gradient histogram; searching a local maximum value and a local minimum value on each direction gradient histogram as candidate key points; for each detected key point, gradient histograms are calculated in small windows around the key point, and a descriptor vector is generated. According to the method, the problem that a traditional feature point detection algorithm cannot detect the feature points by 100% due to the fact that the overlapping area between the images is too small in some splicing application scenes, and the condition of splicing failure is caused is solved.

The embodiment of the invention provides an electrowetting display driving system and related equipment, and belongs to the technical field of electrowetting display. The system comprises a processor unit which obtains an image set and carries out image decoding on the image set to obtain first image data; the illumination decomposition module constructs a Gaussian pyramid according to the first image data, and constructs a corresponding Laplacian pyramid according to the Gaussian pyramid; the illumination reconstruction module performs illumination reconstruction according to the Laplacian pyramid to obtain a corresponding illumination reconstruction pyramid; the illuminance fusion module performs illuminance fusion according to the illuminance reconstruction pyramid and the Gaussian pyramid to obtain optimized illuminance image data; the color rendition module carries out color rendition according to the optimized illumination image data to obtain target image data; the display driving unit is used for generating a display driving signal according to the target image data; and the image display unit performs image display according to the display driving signal. According to the embodiment of the invention, the accuracy of images displayed by the electrowetting display screen can be improved.