48 results about "Exposure fusion" patented technology

The invention discloses a hand-eye-force integration-based catenary cantilever robot assembly method, which comprises the following steps of: synthesizing image sequences of different exposures into a high-quality image based on a self-adaptive multi-exposure fusion technology, identifying a workpiece contour in the image based on a deep learning framework, and calculating a pose of an identified part in a camera coordinate system; the two-dimensional camera and the three-dimensional camera are matched to carry out accurate scanning, an accurate pose deviation matrix of an actual workpiece relative to a theoretical model is obtained through a nearest point cloud registration algorithm, and a corrected moving target pose is calculated; and the robot moves towards the position and posture of the moving target, when the robot is about to make contact with the assembly workpiece, the impedance control mode is switched to conduct cantilever installation, and moment data are continuously monitored in the whole process till installation is completed. According to the method, the robot can intelligently cope with uncertainty in the cantilever assembly process, and the assembly success rate and the assembly efficiency are remarkably improved.

The invention provides a perception-consistent underwater image enhancement method, and relates to the technical field of image enhancement. The method comprises the following steps: firstly, performing dual-channel color compensation, white balance correction and histogram contrast enhancement on an original image to obtain a color enhanced image; secondly, performing median filtering and gamma correction on a brightness channel to generate a multi-exposure image sequence; extracting a detail layer and a base layer of each exposure image, performing weighted fusion based on contrast, exposure and saturation weights, and realizing detail enhancement in combination with rapid steering filtering; and finally, fusing the color enhanced image and the detail enhanced image according to the contrast ratio, the saturation degree and the structural saliency. According to the method, the image quality is improved through dual-path cooperation and dynamic exposure fusion, the method is suitable for scenes such as underwater monitoring, aquatic product monitoring and underwater inspection, complex training is not needed, and deployment on low-power-consumption equipment is facilitated.

The invention discloses a bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.

The invention belongs to the technical field of image processing, and particularly relates to a wide dynamic image fusion method and device for a fast moving scene. A single-lens double-light-path light splitting method is adopted, optical systems with the same type of sensors, the same exposure duration and different aperture coefficients are adopted for double light paths, field angles are matched, scenes are the same, and the error is smaller than 0.5 pixel; the method comprises the following steps: performing video image acquisition, performing wavelet transform on acquired two paths of image data, decomposing two paths of image matrixes to obtain a high-frequency detail image and a low-frequency-width dynamic image, and performing multi-depth-of-field fusion on the high-frequency detail image; performing multi-exposure fusion on the low-frequency wide dynamic image to generate HDR wide dynamic data, and performing tone mapping to form an LDR low-frequency image; and combining the high-frequency fusion detail image and the LDR low-frequency image. According to the method, the image without overexposure, with large depth of field and multiple details can be obtained, and the effects of video processing tasks such as target searching and tracking are effectively improved.

The invention discloses a multi-exposure fusion HDR image reconstruction method based on a flexible lens, and the method comprises the steps: carrying out the image reconstruction of an LDR image sequence through a trained exposure image reconstruction model, and obtaining an HDR reconstruction image; the exposure image reconstruction model comprises a pseudo twin space-frequency domain attention module, an exposure guide fusion module and an exposure recovery module; after features of an input sequence are extracted through an encoder, feature modulation is carried out through a pseudo twin space-frequency domain attention module, weighted fusion is carried out on a modulated feature map through an exposure guide fusion module, illumination recovery is carried out through an exposure recovery module, shallow features of a reference image are fused, and finally an HDR reconstruction image is generated through a decoder. According to the method, the limitation of a traditional model on the fixed number of lenses is broken through, self-adaptive processing on the input sequence of any length is realized, and meanwhile, the detail retention and artifact suppression capabilities are remarkably improved through structural tensor loss.

The invention provides a three-dimensional two-dimensional code surface information reconstruction method, device and equipment and a storage medium, and belongs to the technical field of image processing and three-dimensional reconstruction, and the method comprises the steps: carrying out the contrast enhancement of an initial image set of a three-dimensional two-dimensional code based on an exposure fusion contrast enhancement algorithm, and obtaining a contrast-enhanced image set, the initial image set comprises three-dimensional two-dimensional code images obtained by lighting and shooting the three-dimensional two-dimensional code at four different space angles; normal vector field reconstruction is carried out on the surface of the stereo two-dimensional code based on a four-light-source photometric stereo method and a contrast enhancement image set; converting the established normal vector field into a depth image based on a Poisson solution; and obtaining a reconstructed two-dimensional code image based on the depth image. According to the invention, the technical problem that the error of the surface information of the low-contrast stereo two-dimensional code extracted in a complex environment in the prior art is large is effectively solved.

The invention discloses a fringe projection measurement method for a high dynamic range scene. The fringe projection measurement method can realize high-precision three-dimensional reconstruction of a complex reflection scene without human intervention. The method comprises the following steps: firstly, projecting pure gray-scale maps with different gray scales to calculate the maximum unsaturated exposure time of a camera for a scene, constructing a phase error model for a fringe pattern captured under exposure, and respectively calculating the optimal exposure time of each pixel point under phase error and light intensity constraint conditions; and then, obtaining required exposure times, masks and exposure time by using an iterative optimization algorithm of the masks. And finally, obtaining an optimal fringe pattern for three-dimensional reconstruction by automatically capturing and fusing fringe patterns at different exposure times. Compared with a traditional high-dynamic measurement method, the method has the advantages that high-quality multi-exposure fusion can be completed without human intervention, and high-precision and high-robustness three-dimensional measurement of a high-dynamic-range scene is realized.

The application discloses a kind of based on multiple exposure generation fusion's low-light image enhancement system, belong to low-light image enhancement technical field.System includes: adaptive multiple exposure adjustment module (AMEA), joint prediction gamma coefficient, linear modulation factor and compensation parameter, to input low-light image carries out nonlinear and linear combination transformation, generates multiple luminance, contrast and different color response multiple exposure candidate image;Multiple exposure fusion module (MEF), original low-light image and candidate image channel splicing are carried out, and preliminary enhancement image is obtained by fine-grained weighting fusion of channel fusion coefficient and introduction of perception compensation parameter correction;Color refinement module (CRM), using encoder decoder structure and introducing fast fourier representation in bottleneck layer, realize spatial domain and frequency domain joint modeling, carry out global color calibration and local detail optimization to preliminary enhancement image.The overall network uses lightweight design such as depth separable convolution and dense connection, inference is simple, and can be run in real time in edge device.The application generates by adaptive multiple exposure, structure constraint fusion and frequency domain refinement, effectively restores dark texture, suppresses over-enhancement and color deviation, improves visual naturalness and robustness in complex low-light scene.

A multi-exposure image fusion method belongs to the field of image processing. The method includes the following steps: S1: Calculate the average brightness of each input image and sort them in ascending order of average brightness; S2: Perform intensity mapping on the middle image so that its average brightness is at the middle gray value; for the remaining images, perform intensity mapping according to their ranking numbers so that the input image sequence contains images with various exposure levels; S3: Use the middle image as a reference image, divide each image into blocks, perform dynamic image detection and replacement block by block. S4: Perform multi-exposure fusion on the image sequence that has undergone dynamic detection and replacement to obtain the final fused image. The input image set of this method contains images with various exposure levels, improving the fusion effect; and the pre-dynamic image detection can screen out the moving objects in each image, replace them and perform the final fusion, improving the dynamic range of the image and avoiding the ghosting caused by moving objects.

The present invention discloses a multi-exposure image fusion method based on pyramid convolution, comprising the following steps: Step 1: splicing overexposed and underexposed images by channel and preprocessing them to obtain a system input; Step 2: sending the system input to a decorrelation module to eliminate channel-level and pixel-level correlations in the data; Step 3: performing feature fusion on the output of Step 2 through an encoder and mapping it to a high-dimensional data space; Step 4: performing dimensionality reduction on the fused high-dimensional data through a decoder, reconstructing the fused image, and completing multi-exposure fusion. The multi-exposure image fusion method of the present invention can improve halos and artifacts when fusing extreme lighting scenes, enhance the generalization ability of the multi-exposure image fusion algorithm for extreme scenes, and is widely used in scenes such as underwater and nighttime observation.

The invention discloses a simulation-based high dynamic range image synthesis method, which comprises the following steps of: generating low dynamic range images with different exposure degrees based on a CARLA simulator, and synthesizing a plurality of low dynamic range images into a high dynamic range image by using a multi-exposure fusion algorithm, thereby constructing a large-scale automatic driving high dynamic range simulation data set. According to the invention, through a multi-exposure fusion algorithm, low-dynamic-range images of multiple exposures are concisely and efficiently fused, a high-quality high-dynamic-range image which retains details of a dark part and a bright part at the same time is obtained, and the synthesis speed is relatively high. The method supports the synthesis of a large-scale automatic driving high-dynamic range policy data set, and is used for the research of related tasks.

The application discloses a kind of self-adapting multiple exposure fusion methods and systems based on surface structure light three-dimensional measurement, the method is first based on initial exposure image calculation modulation chart and maximum gray value, and according to modulation degree division multiple regions, and through the gray value correction and local information entropy weighting of pixel, the average gray of each clustering is accurately calculated, and then the optimal exposure time is solved for different regions, in image fusion stage, by analyzing the gray value distribution of high exposure image Dynamic determination of overexposure threshold, and with the pixel modulation degree under each exposure as the core basis, the fusion weight is calculated, and finally the global high-quality fringe image is generated.The application solves the problem that the reconstruction accuracy is low due to local overexposure or underexposure of high dynamic range objects, and significantly improves the three-dimensional measurement accuracy and automation level of high dynamic range objects.

The application provides a deep multi-modal image fusion method and system, and relates to the technical field of image processing; the method comprises the following steps: respectively extracting features of an overexposed image and an underexposed image, respectively obtaining dictionary features and sparse features of the overexposed image and dictionary features and sparse features of the underexposed image, updating the dictionary features and the sparse features of the overexposed image and the underexposed image through a total target function, respectively extracting common features from the updated dictionary features and the sparse features through a nonlinear function, extracting features according to the extracted common features, obtaining an overexposed fusion image and an underexposed fusion image and performing fusion processing, obtaining a reconstructed image and comparing the reconstructed image with a standard image to generate an evaluation index. The total target function is iteratively changed to perform transformation learning, higher-quality sparse features are extracted to capture important internal properties of the image to reconstruct the image, and the fusion of multi-modal images is realized.

The embodiment of the invention provides a multi-exposure image fusion method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of image processing. According to the method, distinguishing and modeling of a moving object and a static background in a dynamic scene are achieved by generating a moving area weight map, a median threshold bitmap, a superpixel area map and a basic weight map. The median threshold bitmap and the superpixel area map are used for correcting the motion area weight map, the space consistency and the boundary fitting degree of motion detection are improved, motion interference in the basic weight map is eliminated through the final motion weight map, and an accurate fusion weight map is obtained. On this basis, multi-exposure fusion is completed in combination with an original image sequence, ghosting artifacts caused by object movement can be effectively suppressed while highlight and shadow details are reserved, and the quality and authenticity of the fused image are improved.

The invention relates to the technical field of optical imaging, in particular to an image highlight removing method and system based on virtual polaroid and virtual exposure fusion, and the method comprises the steps: obtaining a Stokes parameter image of a target scene, dividing highlight region blocks, preliminarily removing highlight of the highlight region blocks, and removing highlight in combination with a virtual exposure image. According to the method, the virtual polarization filtering technology and the virtual exposure fusion technology are creatively combined, the advantages of the virtual polarization filtering technology and the virtual exposure fusion technology are fully exerted, a brand-new and efficient image highlight removal method is formed, the projector and a light field camera do not need to be combined in the whole process, and the image highlight removal efficiency is improved. According to the invention, the method does not need to employ a plurality of light sources or detectors to obtain images under different visual angles or illumination conditions, overcomes the harsh implementation conditions of the traditional highlight removal technology, improves the reliability and uniformity of highlight removal, and solves the problems of harsh highlight region removal conditions and poor universality in the prior art.

Disclosed is a server apparatus for image processing, the server apparatus comprising an input module, the input module configured to obtain input image data, the input image data comprising a plurality of differently exposed images; an image processing module configured to process the plurality of differently exposed images to generate a composite image; wherein the image processing module comprises a neural augmented multi-scale exposure fusion function, the neural augmented multi-scale exposure fusion function comprises: a plurality of levels; one or more weight maps of the plurality of differently exposed images, each weight in the one or more weight maps associated with an image of the plurality of differently exposed images; a plurality of first pyramids corresponding to the plurality of levels, the plurality of first pyramids generated based on the one or more weight maps; and a plurality of hybrid pyramids, each of the plurality of hybrid pyramids constructed based on at least one edge- preserving smoothing function using the plurality of first pyramids as input.

The present invention designs a leaf reflective coding image stripe repair system and method combined with multi-exposure fusion. The repair system includes: an image acquisition module, an image fusion module, a mask image production module, an image annotation module, a generator module and a discriminator module; the image acquisition module collects leaf stripe images of different exposures, and the image fusion module fuses leaf stripe images of different exposures under the same posture to obtain an exposure-fused leaf stripe image; the mask image production module produces an irregular mask image; the image annotation module annotates the reflection and stripe confusion and missing parts in the leaf stripe image to obtain an annotated leaf stripe image, namely a label image; the generator module uses the leaf stripe image fused by the multi-exposure algorithm and the irregular mask image as input to perform feature extraction and reconstruction to generate a finely repaired leaf stripe image; the discriminator module distinguishes the leaf stripe image generated by the generator from the label image.

The application relates to the field of medical image processing, in particular to an examination image processing method based on multi-exposure fusion. The method comprises the following steps: collecting multiple groups of images with different exposure intensities under the same examination visual angle; segmenting throat key anatomical structures in each group of images to obtain initial boundary coordinates; generating a triangular mesh covering the key region in the range of the initial boundary coordinates as a feature unit; calculating brightness feature values including brightness average values and brightness gradient values for the feature unit; establishing the association of the feature units among multiple images according to the brightness average values, and selecting the clearest expression of each boundary based on the brightness gradient values, and finally fusing all the optimized boundaries to form a strengthened result. The method significantly improves the detail performance of highlight and shadow areas in the image, effectively reduces the work burden of multi-image comparison and analysis while ensuring the diagnostic accuracy.

The invention discloses a high-reflection surface three-dimensional reconstruction method and device based on virtual exposure fusion. The method comprises the following steps: collecting a plurality of exposure image groups of a to-be-reconstructed target; one exposure image group corresponds to one projection frequency and comprises a plurality of exposure image subgroups; generating a corresponding intermediate exposure image group based on each exposure image subgroup, and then performing weighted fusion to obtain a corresponding virtual exposure image; fusing the virtual exposure image corresponding to each middle exposure image group and the high-exposure image and the low-exposure image in the corresponding exposure image subgroup by adopting a Laplacian pyramid multi-scale fusion mode to obtain a corresponding fused image; based on each fused image, obtaining an absolute phase distribution diagram through a phase shift method and a multi-frequency heterodyne method; and generating a three-dimensional point cloud of the to-be-reconstructed target according to the absolute phase distribution diagram, thereby providing a high-reflection surface three-dimensional reconstruction method which is wide in application range, simpler in process and higher in precision.

An exposure control method includes: calculating a long-exposure brightness mean value of pixels of darker (N−X) kinds of brightness among N kinds of brightness values of a long exposure image, wherein a total number of pixels of brightest X kinds of brightness values among the N kinds of brightness values is a long-exposure bright pixel number; when the long-exposure brightness mean value doesn't approximate to a long-exposure target brightness mean value, adjusting exposure setting for generating / modifying the long exposure image to make the two brightness mean values be similar / equal; determining brightest K pixels of a short exposure image, wherein the K is equal to the long-exposure bright pixel number; calculating a short-exposure brightness mean value; and when the short-exposure brightness mean value doesn't approximate to a short-exposure target brightness mean value, adjusting exposure setting for generating / modifying the short exposure image to make the two brightness mean values be similar / equal.

The application belongs to the technical field of image processing, and particularly relates to a multi-exposure fusion method for separating illumination and motion; low-exposure images, medium-exposure images and high-exposure images are subjected to illumination preprocessing and input into a fusion model; the low-exposure images and the high-exposure images are mapped to the illumination level of the medium-exposure images and normalized according to first gamma values and second gamma values, to obtain first new medium-exposure images and second new medium-exposure images, and the new medium-exposure images are subjected to motion alignment with the medium-exposure images; the first new medium-exposure images and the second new medium-exposure images are subjected to inverse gamma transformation respectively, to obtain new low-exposure images and new high-exposure images, and the new low-exposure images and the new high-exposure images are subjected to illumination fusion with the medium-exposure images to obtain a high dynamic range image, loss is calculated, and model parameters are trained by back propagation; a to-be-processed image is obtained, and a corresponding high dynamic range image of the to-be-processed image is obtained by using the trained fusion model; the application adopts the idea of separating illumination and motion, so that the high dynamic range image retains more information of the input image, and ghosting is reduced.

This invention relates to the field of image enhancement technology and discloses a low-light image enhancement method and system for 5G remote control systems based on deep Retinex decomposition. The method acquires and preprocesses the original low-light image from the front-end device of the 5G remote control system, inputs it into a lightweight multi-scale illumination sensing network to separate the initial illumination and reflection components, corrects the initial illumination component using a dynamic dual-branch exposure fusion module, processes the initial reflection component using a residual noise-detail co-optimization module, and then reconstructs and enhances the image through a halo and color joint correction mechanism. The image is then encoded, compressed, and transmitted to the remote control center via a 5G private network. This invention solves the core problems of underexposure and overexposure, noise interference, detail loss, and wasted bandwidth in low-light images in 5G remote control scenarios, achieving lightweight real-time enhancement at the edge, ensuring image quality and transmission efficiency, and providing reliable support for remote control decisions.

The invention relates to the technical field of image enhancement processing, and particularly provides a camera low-light image enhancement method and system based on dynamic exposure fusion deep learning, and the method comprises the steps: generating a group of control signals with continuous virtual exposure parameters, and driving an image signal processing program to simulate and generate a corresponding short-time image sequence; analyzing a signal reliability measurement diagram of each pixel position in the short-time image sequence under different virtual exposure parameters; performing selective aggregation on features of all corresponding pixel positions in the short-time image sequence in the feature domain to generate an intermediate fusion image; and interacting the features of the intermediate fusion image with a task feature map of an embedded external visual task priori knowledge parallel processing path to generate a group of task-perceived attention masks. The system comprises an image signal processing module, an intermediate fusion image module and a feature interaction module. According to the method, a complete processing link from physical signal simulation to semantic feature maintenance is formed.

The present invention discloses a method for measuring leaf area index based on multi-exposure fusion. First, a fisheye camera is used to capture multiple canopy images at the same sampling point. The canopy images are then converted into canopy grayscale images. The canopy grayscale images are then subjected to multi-exposure fusion processing to obtain a multi-exposure fused image. The multi-exposure fused image is binarized, and then the canopy porosity at multiple angles is extracted from the binarized image to invert the leaf area index.

The application relates to a low-illumination gray-scale image enhancement and denoising method, which comprises the following steps: constructing an optimal exposure ratio image according to an input low-illumination gray-scale image; decomposing the low-illumination gray-scale image and the optimal exposure ratio image by adopting a LatLRR method to obtain a low-rank part and a significant part; constructing a low-rank part weight and a significant part weight according to the obtained low-rank part and the significant part; performing normalization processing on the obtained low-rank part weight and the significant part weight to obtain a normalized weight; and performing image reconstruction and synthesis according to the low-rank part, the significant part and the normalized weight to obtain a multi-exposure fusion enhanced image. The application can enhance and denoise a single low-illumination image, restore structure information, maintain details, avoid block effects and reduce the calculation complexity.

The invention discloses a self-adaptive multi-exposure fusion three-dimensional measurement method for a high dynamic range scene. The method comprises the following steps: firstly, acquiring high-frequency phase shift fringe patterns at different exposure times, and finding out initial exposure time for preventing effective measurement areas in all phase shift patterns from overexposure through time domain light intensity overexposure detection operation; taking the high-frequency phase shift fringe pattern of the initial exposure time as an initial image of multi-exposure fusion, adaptively reserving a fringe region with high phase quality according to a modulation degree condition through a noise-modulation degree model, and introducing a time domain superposition principle to estimate the next exposure time of the multi-exposure fusion; and finally, calculating an effective measurement area of the current fused fringe image by using a reference plane phase based on depth constraint, and continuing to perform multi-exposure fusion until the effective measurement area meets a modulation degree condition, thereby completing three-dimensional measurement of the whole high dynamic range scene. According to the invention, the high-quality fringe pattern is obtained through adaptive multi-exposure fusion, the total exposure time is significantly reduced by introducing the time domain superposition technology, and high-precision and high-efficiency full-automatic three-dimensional measurement of a high-dynamic-range scene is realized.

The application discloses an automatic multi-exposure selection method based on fringe projection. Firstly, a projector is used to project a phase-shift fringe pattern to a measured scene, and a camera synchronously collects a group of fringe images with a preset exposure value. A pseudo-white image is obtained by using a highest-frequency fringe image. An overexposure area with a brightness greater than a preset threshold value in the pseudo-white image is found, and a seed filling algorithm is used to divide the overexposure area into a plurality of sub-areas. The pixel number and average brightness of the largest overexposure sub-area are used to iteratively calculate an optimal initial exposure time, so that the initial exposure is as large as possible and the measured scene has no obvious overexposure area under the initial exposure. An automatic multi-exposure algorithm based on a fringe image modulation degree is used to calculate a multi-exposure number and automatically iterate subsequent exposure times, high-quality absolute phases under the exposure times are fused to obtain a multi-exposure fusion absolute phase. The calibration parameters of a binocular camera are used to perform stereo rectification on the absolute phases of left and right cameras. The absolute phases of the left and right cameras are phase-matched to obtain a two-dimensional disparity map, and high-speed and high-precision three-dimensional topography measurement is realized.

The invention discloses a multi-exposure fusion angle resolution scattering micro-nano structure measurement method, and relates to the technical field of integrated circuit measurement. The method comprises the following steps: acquiring a multi-exposure sequence image set, and modeling a camera response function CRF; estimating an overexposed pixel value by adopting a linear extrapolation method based on a tangent line at the endpoint of a response curve, and calculating the confidence coefficient of an extrapolated pixel; when the confidence coefficient of the extrapolated pixel is lower than the basic confidence coefficient, enhancing the extrapolated pixel by using a segmented conservative estimation strategy; a correction image set and a corresponding confidence coefficient image set are obtained through iterative processing; carrying out weight distribution on the images in the corrected image set to obtain a corresponding normalized weight set; and performing multi-scale fusion according to the normalized weight set and the corrected image set to obtain a fused image. Pixel irradiance of an overexposure area is effectively reconstructed by performing pixel-level correction on an image, and high-fidelity fusion of Fourier space intensity is realized by performing multi-scale fusion on the image.

The present disclosure relates to an image exposure fusion method, device, and storage medium. The method includes: obtaining a sequence of images to be fused, the sequence comprising multiple images to be fused, each of which is captured at different exposure levels for the same scene; calculating a fused Laplacian pyramid based on the sequence of images to be fused; replacing the brightness distribution of the second top image of the fused Laplacian pyramid with the brightness distribution of the first top image of the target Laplacian pyramid to obtain a target fused Laplacian pyramid, wherein the target Laplacian pyramid is generated based on the target image to be fused in the sequence of images to be fused; and reconstructing a target image based on the target fused Laplacian pyramid. Using this method, the image brightness inversion problem can be improved, thereby making the global brightness distribution of the target image more reasonable.

This invention provides a multi-exposure image fusion system, method, and electronic device. The system includes: an image sequence alignment and correction module, comprising a selection submodule and an image sequence alignment and correction network. The selection submodule is configured to select a reference exposure level image, and the image sequence alignment and correction network is configured to align and correct other exposure level images based on the reference exposure level image; an image fusion deep network, configured to fuse the aligned and corrected multi-exposure image sequence to generate a multi-exposure fused image; and a visual quality-driven perception optimization module, configured to evaluate the multi-exposure fused image using a fused image visual quality evaluation model and optimize the image fusion deep network based on the evaluation results. This invention, by performing multi-exposure image fusion based on a deep network, can effectively capture image details and better understand the content of multi-exposure images, thereby achieving fast and efficient multi-exposure image fusion.