403 results about "Normalization (image processing)" patented technology

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

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing image adaptive identification method and system for territorial space planning, and the method comprises the steps: obtaining a multi-source remote sensing image data set of a research region, feature extraction, cloud detection, quality evaluation and adaptive preprocessing are carried out; carrying out prototype network coding, calculating a category prototype and probability, and supporting fine tuning of a set; carrying out multi-scale cavity convolution and category scale attention fusion; evaluating the adaptability score of the comprehensive fusion feature map set, and carrying out weighted fusion, classification and normalization; change detection is carried out, stable and change regions are segmented, and time sequence context features are extracted and constrained optimization is carried out; entropy is fused, a boundary is decided, uncertainty is estimated, and weighted fusion is carried out according to a change area; conditional random field optimization, confidence level grading and connected domain identification are carried out; the automation level, the adaptive capacity and the recognition reliability of remote sensing monitoring of territorial space planning are improved.

The invention relates to the technical field of medical image processing, and particularly discloses a medical image segmentation method with adaptive receptive field and feature correction, which comprises the following steps: (1) acquiring an original medical image and a segmentation label thereof, and constructing a training and testing data set; (2) carrying out size normalization and enhancement processing on the image; (3) establishing an improved U-shaped encoder-decoder segmentation network, introducing an adaptive branch mixed shape convolution module in a shallow layer, and improving edge and texture feature modeling capability by adopting a multi-branch banded convolution and channel attention mechanism; (4) a residual directional feature interaction module is introduced into a deep layer, a spatial dependency relationship is modeled through an information interaction structure in the horizontal and vertical directions, and the direction sensing ability of the heterostructure is enhanced; and (5) completing network training and reasoning, and outputting a segmentation result. The method gives consideration to the calculation efficiency and the segmentation precision, and is suitable for the automatic segmentation task of various types of medical images with complex structures.

The invention relates to the field of computer technology and medical image processing, in particular to a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism. In order to solve the problems of scarcity of annotation data, weak model generalization ability, inaccurate segmentation boundary and the like in a current medical image segmentation task, the invention provides a medical image segmentation method fusing random region cutting enhancement and a pseudo label semi-supervised mechanism, which is called an RCDE segmentation model. The model adopts a shared encoder and double decoder structure, combines a structure-level disturbance generation strategy, enhances the perception ability of the model for image structure change by mixing and recombining labeled images and unlabeled images, generates pseudo labels by utilizing a teacher network, and introduces a dynamic confidence coefficient screening mechanism, so that low-quality pseudo labels are effectively eliminated, and the robustness of the model is improved. The training stability and the pseudo-supervision effect are improved, preprocessing such as size normalization and image enhancement is carried out on the image, and the consistency and robustness of model input are improved.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention relates to the field of computer vision and image processing, and discloses a remote controller liquid crystal screen image detection method based on computer vision, which comprises the following steps: S1, acquiring an original image containing a remote controller, and carrying out gray level conversion and filtering denoising processing on the original image to obtain a preprocessed image; s2, calculating the local information entropy and the change rate of the image through a sliding window under multiple scales based on the preprocessed image, generating a multi-scale entropy gradient heat map, and screening out a candidate region with high entropy difference as a liquid crystal display region according to a set threshold value; and S3, carrying out vectorization partitioning on the candidate region, and constructing an image matrix. According to the invention, by introducing an image preprocessing mode of low-rank sparse decomposition and normalization processing, the expression ability of structural information in the character image is effectively enhanced, and the feature extraction accuracy under the conditions of complex background, low character contrast and the like is improved.

The invention relates to the technical field of image processing, in particular to a printing image-text defect detection method based on machine vision, and the method comprises the steps: obtaining an RGB image of a to-be-detected printed matter, and carrying out the Lab space conversion, and obtaining a target image; performing normalization processing on the L channel value of each pixel point in the target image to obtain a normalized brightness value so as to divide the pixel points in the target image into a preset number of illumination level intervals; obtaining a color fluctuation statistical parameter of any one illumination level interval, and obtaining an optimal color difference threshold value corresponding to any one illumination level interval; according to the optimal color difference threshold value corresponding to the illumination level interval to which each pixel point in the target image belongs, performing Lab color space template comparison on the target image to obtain an image-text color difference defect result of the to-be-detected printed matter, thereby improving the accuracy of image-text printing defect detection based on the color difference threshold values.

The invention belongs to the technical field of image processing, and discloses a multi-feature enhancement fusion Mama image denoising method, which comprises the following steps: constructing a Mama denoising model for image denoising processing, the Mama denoising model comprising a shallow feature extraction module, a depth feature extraction module and an image reconstruction module; the shallow layer feature extraction module is used for extracting shallow layer features of the input image and performing normalization processing on the shallow layer features; the depth feature extraction module comprises a plurality of DFEGs and a sixth convolutional layer, and feature extraction is performed on the normalized shallow layer features step by step through the plurality of DFEGs; according to the invention, through a plurality of DFEGs in the depth feature extraction module, gradual feature extraction from a shallow layer to a deep layer is realized, and through cooperation of the Mama long-distance dependence capture unit, the local residual error module LRB and the channel attention mechanism CAB, the problem that global structure and local detail recovery are difficult to consider at the same time in a complex noise environment in the prior art is effectively solved.

The invention provides a cultural relic crack monitoring system and method based on image processing, and relates to the technical field of image processing. The system is composed of a multispectral image acquisition module, an environment calibration module, an image processing module, a contour recognition module, a crack recognition module, a time sequence evolution analysis module and an output module. The calibration module performs reflectivity normalization according to the material spectrum and real-time illumination and inhibits specular reflection; the image processing module uses multi-scale Retinex to enhance the crack contrast; the contour recognition module is used for extracting candidate contours by using improved Canny; the crack identification module introduces a lightweight CNN to carry out texture analysis and generates crack and texture data; the time sequence evolution analysis module completes multi-moment crack registration and morphological parameter difference; and the output module summarizes the data to generate a chart and a monitoring report. The false detection rate of a traditional method is reduced.

The invention discloses a video anti-shake method and system based on multi-scale fusion and adaptive smoothing, relates to the technical field of video image processing, and aims to effectively solve the image quality problem caused by shake in a video shooting process. Gradient histograms and wavelet energy distribution characteristics of video frames are extracted through graying and normalization processing, and the gradient histograms and the wavelet energy distribution characteristics are input into a jitter type recognition network to recognize translation, rotation and Z-axis jitter probabilities. And further extracting motion, frequency domain and edge features, and generating multi-modal coupling features through combination of a dynamic feature interaction network and a dot product attention mechanism. And constructing a motion trajectory by using the features, optimizing the trajectory by using a texture perception double-layer smoothing strategy, introducing an adaptive penalty term into a dynamic planning cost function, and outputting a smooth motion compensation parameter. And finally, processing the boundary region through motion compensation and image extrapolation to generate an anti-shake video frame. Through multi-scale feature fusion and a self-adaptive smoothing strategy, the video anti-shake effect is effectively improved, and the method is suitable for complex scenes.

The invention relates to the technical field of image processing, and discloses a defect detection method and device for a wafer chip. The method comprises the following steps: preprocessing an original wafer image, wherein the preprocessing comprises at least one of the following items: contrast enhancement, noise suppression and smoothing, sharpening enhancement and normalization processing; dividing the preprocessed wafer image into a plurality of chip areas, and extracting a feature vector of each chip area by using a convolutional neural network to form an initial node feature matrix; based on the spatial position relationship between the chips, constructing a spatial adjacency graph of the wafer; and inputting the initial node feature matrix and the spatial adjacency graph into a defect detection model, and outputting a wafer-level defect detection result. According to the method, comprehensive modeling of defects in spatial distribution and associated feature levels can be realized, and the accuracy and robustness of detection are improved.

The invention relates to the technical field of image recognition, in particular to a blind sidewalk recognition method based on image processing. The method comprises the following steps: collecting a ground scene image, and carrying out noise suppression and illumination normalization processing to obtain a ground scene image to be processed; inputting the to-be-processed ground scene image into a pre-constructed convolutional neural network model to identify feature textures of the blind sidewalk bricks; dividing a blind sidewalk area in the ground scene image to be processed by using the blind sidewalk brick feature texture, and determining a direction gradient feature of the blind sidewalk area; executing blind sidewalk direction consistency constraint based on the direction gradient features to collect blind sidewalk structured path segments; recognizing a blind sidewalk fracture area based on directional gradient features; and performing cross-frame target tracking according to the structured path segment of the blind sidewalk, and outputting a continuous blind sidewalk trajectory. The automatic recognition rate of the blind sidewalk area is improved based on the image recognition technology, and the continuous recognition capacity of the blind sidewalk path in the complex illumination and shielding environment is enhanced.

The invention belongs to the field of instrument identification, and particularly relates to a pressure gauge identification method and equipment in an industrial valve pressure testing process. The method comprises the following steps: extracting an instrument image from a collected video stream, and carrying out distortion correction and illumination normalization processing; feature extraction is carried out on the processed image, instrument types are identified based on a training model of multiple types of instruments, and an instrument image with angle deformation is corrected through a spatial transformation network; performing spatial sorting on the identified instrument images according to the coordinates of the central point of the detection frame, and generating a data array containing instrument information; and finally, performing enhancement processing on a single instrument image in the data array, positioning a dial plate area, performing perspective correction, determining geometric parameters of the dial plate, calculating an original reading based on the parameters, and outputting a final reading in combination with inclination compensation. Through the image processing and recognition steps, accurate recognition and reading of the pressure gauge in the industrial valve pressure testing process are achieved, and the recognition efficiency and accuracy are improved.

The invention provides a thoracic surgery pathological section image super-resolution reconstruction method, and relates to the technical field of image processing, and the method specifically comprises the steps: collecting a high-resolution thoracic surgery pathological section image, and generating a low-resolution image; constructing a local directional dot matrix tensor and a response adjustment factor based on the low-resolution image, calculating an angle response aggregation kernel tensor, and performing segmented mapping in combination with a nonlinear response reconstruction function to complete directional fusion normalization so as to obtain a directional aggregation enhanced image; a robust local reference value and local texture energy are calculated in a neighborhood range, a structural strength weight is generated in combination with a variance balance parameter and a contrast balance parameter, a difference amplification item is constructed, image fusion with direction aggregation is enhanced through a residual mode, and a structure guide enhanced image is obtained; carrying out weighted mean and variance calculation to generate a difference regulation factor, and combining convolution smoothing and nonlinear amplitude limiting processing to obtain a structure contrast mapping image; and finally, inputting the image into an image reconstruction module to realize resolution improvement.

The invention discloses a multi-target license plate recognition method based on a visual attention mechanism, and belongs to the technical field of image processing and mode recognition, and the method comprises the steps: obtaining an input image, carrying out the multi-scale feature extraction, and generating a multi-scale feature map; performing spatial saliency calculation and normalization processing on the multi-scale feature map to generate an attention map; carrying out region division on the input image, and adopting differentiated image preprocessing strategies for different regions to generate a preprocessed image; based on the preprocessed image and the attention map, multi-target detection is carried out through a target detection network, candidate area screening is carried out, and a candidate license plate area is generated; and performing binarization processing, character segmentation and feature recognition on the candidate license plate region, and performing verification in combination with context information to generate a license plate recognition result. The attention map is generated by adopting a visual attention mechanism, differentiated image preprocessing and multi-target detection are guided according to the attention map, and multi-target license plate recognition can be completed in a complex scene.

The invention discloses a landslide image instance segmentation method based on a dual adaptation mechanism, relates to the technical field of image processing, and ensures that a model can have a better effect and numerical stability on data from different sources through a complete process from multi-source data collection to data normalization processing. The designed model is based on a multi-scale state alignment mechanism, in the forward process of the model, exponential moving average fusion is carried out on feature information in the same level, transmission fusion is carried out on feature information of different levels, feature robustness is enhanced, and error accumulation caused by local deviation is reduced. A meta-context incremental learning mechanism is designed, and input data are dynamically converted into a series of key value vector sequences. In the reasoning process of the model, data distribution different from a training domain is dynamically recognized, a gradient descent process is implicitly executed according to the characteristics of data, and model parameters are finely adjusted, so that the characterization capability is greatly improved, and efficient and robust instance segmentation is realized.

The invention discloses a sports equipment management personnel identification method, system and equipment, and the method comprises the steps: starting and initializing a camera through detecting a sports equipment use trigger signal, and stopping automatic focusing; and shooting multiple frames of images at different focal lengths according to a preset interval, determining a clear area by using a gradient magnitude algorithm, and performing image registration and fusion to form a composite image. And if the whole area is not covered, shooting and splicing are carried out again. And through composite image splicing, distortion cutting, noise reduction and normalization processing, a final image is output for determining the identity of a person. According to the invention, through multi-frame shooting and accurate image processing, a high-quality image is obtained, and information limitation and quality defects of a single image are effectively avoided. Various algorithms are fused to ensure that the image is clear, complete and standardized, the accuracy and reliability of personnel identity recognition are remarkably improved, an accurate image basis is provided for sports equipment management, the normalization and safety of equipment use are guaranteed, meanwhile, the image collecting and processing efficiency is improved, and the management cost is reduced.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention relates to the field of artificial intelligence and medical image processing, in particular to a mammary gland pathology visual model establishing method based on multi-model fusion and combined distillation, which comprises the following steps: performing tissue segmentation and dyeing normalization on a full-slice image; inputting the image blocks into a pre-training teacher model of a plurality of freezing parameters in parallel to extract high-dimensional features, and generating unified enhanced features through a learnable feature fusion network; constructing a student model, and performing end-to-end training by using a joint loss function including feature simulation, logic output distillation and multi-task supervision; and connecting a plurality of task specific prediction heads to the student model, and realizing full-slice-level multi-task diagnosis and treatment prediction through an aggregation strategy. According to the technical scheme, efficient knowledge migration and multi-task cooperation can be achieved, and the accuracy, generalization ability and reasoning efficiency of mammary gland pathology analysis are remarkably improved.

The invention relates to the technical field of medical image processing, and particularly discloses an intelligent segmentation system for a kidney tumor in a CT image, and the system comprises an image preprocessing module, an ROI automatic detection module, a kidney tumor segmentation module, a segmentation result post-processing module, a visual interaction module, and a model updating module. The system improves image quality through normalization and filtering, uses a target detection network to position a kidney area, realizes precise tumor segmentation based on an integrated channel attention mechanism and a U-Net network guided by structure priori, optimizes a boundary in combination with a conditional random field, adopts a multi-plane fusion strategy to improve three-dimensional consistency, and calculates tumor volume. The system supports federal learning update, improves the cross-mechanism generalization ability, has the advantages of high segmentation precision, strong boundary reducibility, good structural rationality, strong clinical deployment and the like, and is suitable for kidney tumor auxiliary diagnosis and quantitative analysis.

The invention discloses a method for generating an image description text based on a large model, which relates to the technical field of image processing, and comprises the following steps: an image preprocessing step: dividing a target level through semantic segmentation and extracting key visual information by adopting hybrid denoising and self-adaptive normalization; a feature extraction step: fusing the multi-scale visual features and the semantic features, and generating a high-dimensional fusion feature vector through cross-modal alignment; a large model initialization and adaptation step: loading the pre-training model and performing incremental fine tuning, and dynamically adjusting the Prompt template; a text generation step: generating candidate texts through logic constraint and beam search; and a text optimization adjustment step of outputting a final description text based on multi-dimensional evaluation and user preference iterative correction. According to the method, the semantic matching degree, logic coherence and common sense accuracy of image description are improved, multi-element scenes are adapted through dynamic adaptation and iterative optimization, and high-quality text description support is provided for high-precision and diversified scenes.

The invention provides a foggy day image target detection model training method and system and a detection method, and relates to the technical field of image processing, and the image visual target detection method comprises the steps: obtaining an original image sample; performing channel normalization processing on the original image sample; carrying out defogging processing on the original image sample after channel normalization processing to obtain a defogged image sample; and respectively inputting the original image sample and the defogged image sample after channel normalization processing into a skeleton network of a depth model for model training to obtain a target detection model. According to the training method and system and the detection method provided by the invention, the defogged picture features and the degraded picture features are combined, so that the model is easier to converge, and the target detection model obtained by training has a better effect in processing a foggy day scene.

The invention relates to the technical field of image processing, in particular to a clinical ultrasonic image auxiliary screening system based on deep learning. The system comprises an ultrasonic image preprocessing module, a lesion area segmentation module, an image lesion marking module and a lesion auxiliary screening module, and can obtain a clinical ultrasonic image and perform graying processing and neighborhood gray level equalization processing to generate a clinical ultrasonic equalization image; performing pixel normalization and tissue boundary fuzzy-based lesion region segmentation on the clinical ultrasonic equalization image to generate clinical ultrasonic image lesion region blocks; carrying out image lesion marking through the clinical ultrasonic image lesion area block to generate a clinical ultrasonic lesion marking image set; and constructing an image lesion auxiliary screening model and carrying out lesion auxiliary screening so as to output lesion positions, lesion types and lesion severity corresponding to lesions on the clinical ultrasonic image. According to the invention, accurate analysis of clinical ultrasonic images can be realized, so that lesion screening can be efficiently completed in an assisted manner.

The invention relates to the technical field of computer vision and image processing, in particular to a cross-day-and-night boundary target thermo-optic joint detection method, which comprises the following steps of: firstly, acquiring a visible light image and an infrared image and unifying the visible light image and the infrared image to an image plane reference system; extracting topological features and multi-scale energy features from the input, fusing the topological features and the multi-scale energy features to generate a feature map, and outputting a central heat map, target size regression and sub-pixel offset by adopting anchor-frame-free detection; implementing optimal transmission correction according to an evidence field obtained by normalization of a cross reconstruction residual field to obtain a correction heat map and an initial candidate; triggering a fixation area by using the shape correction heat map and the initial candidate, executing super-resolution and secondary detection in the area, and performing affine reprojection and primary detection fusion to form an updated heat map and a fusion candidate; and in combination with uncertainty and topological consistency, a final detection set is output by using a conditional random field and non-maximum suppression. The method is stable in low-contrast, small-target and strong-interference scenes.

The invention discloses a medical image artifact identification and elimination method based on a big data technology, and relates to the technical field of medical image processing, and the method comprises the steps: carrying out the preprocessing of collected image data based on gray normalization, and then carrying out the semantic segmentation and ROI positioning of the image content; and performing lesion segmentation on the positioned image content, selecting lesion features for quantification and fusion, performing model verification, and performing distributed deployment on the verified lesion segmentation model. According to the method, the problems of high missed detection rate of small nodules and high missed diagnosis risk of malignant lesions are solved through the lesion detection model, the false positive rate is reduced, the recall rate of the malignant lesions is improved, and through the lesion segmentation model, the segmentation adaptability to lesions of different sizes is improved, clear segmentation boundaries are obtained, surgical planning is assisted, and boundary positioning errors are reduced.

The invention relates to the technical field of medical image processing, in particular to a DICOM sequence intelligent normalization and sorting method and system, and the method comprises the steps: obtaining an original DICOM file; analyzing a key DICOM tag of the original DICOM file to obtain a direction tag and body position information, and based on the direction tag, realizing normalization based on a patient coordinate system and performing direction correction; projecting the coordinates of all the slices to a normal vector direction to reconstruct a layer sequence, and counting a slice interlayer spacing sequence to estimate a real interlayer spacing based on adjacent projection difference values; identifying and rejecting abnormal slices based on the abnormal comparison target label and the slice interlayer spacing sequence; and for a multi-sequence set of the same check, extracting a sorting feature vector corresponding to each sequence, and selecting a sorting rule in combination with scene complexity to obtain a final sorting key for representing a sorting result of the phase dimension and / or the time dimension. The method has the effect of solving the problems that existing DICOM sequences are inconsistent in direction, disordered in layer sequence, difficult in time phase recognition and the like.

The invention relates to the technical field of image processing, and discloses a stomach tumor image segmentation method and system based on a hybrid model, a terminal and a storage medium, and the method comprises the steps: carrying out the resampling of a stomach tumor image, carrying out the normalization processing of voxels, and obtaining a compressed image set of each target object; performing iterative optimization on the compressed image set by using a generative adversarial network to obtain a target false image set, inputting the target false image set into an encoder of a target segmentation network, fusing the target false image set to obtain dimension fusion information, screening multiple pieces of expert data through a gating mechanism, and generating a corresponding fusion weight; and training the tumor segmentation model by utilizing expert data so as to predict the compressed image set and output an image prediction result. According to the method, iterative optimization is carried out on the image, finally, a sample with better quality is input into the model to participate in training, finally, the generalization ability and robustness of the model can be improved on the premise of not enhancing the labeling cost, and the accuracy of a prediction result is improved.

The invention relates to the field of image processing, in particular to an infrared image bilateral filtering method based on field programmable gate array (FPGA) optimization, which comprises the following steps: acquiring data through a hierarchical architecture of'on-chip BRAM cache + distributed register cache ', intercepting pixels in combination with a sliding window, calculating a spatial weight by using an LUT look-up table, and logically calculating a gray scale weight in real time. And carrying out weighted summation and normalization output. According to the method, the processing delay of the infrared image with the resolution of 1024 * 1024 is reduced to be less than 50ms, the frame rate is increased to be more than 60fps, and the method is suitable for a real-time target recognition scene. Through the combination of the optimized bilateral filtering algorithm and the efficient hardware architecture, the processing delay is remarkably reduced, the real-time processing of the high-resolution infrared image is realized, and the real-time performance is remarkably improved.

The invention relates to the technical field of image processing, and discloses an image brightness improving method based on a neural network, and the method comprises the steps: data preparation: obtaining an image sample set which comprises a plurality of collected image samples of different illumination conditions, portrait features and background environments, marking the ideal brightness effect of each image sample as a target output; model training: constructing a neural network model, training the neural network model by using the image sample set, and optimizing an image brightness mapping relation by adjusting a network structure and a loss function; preprocessing: carrying out size limitation, normalization processing and face region detection on the input image; and brightening realization: inputting the preprocessed image into the trained neural network model, and outputting the image after brightness enhancement. According to the method, through neural network architecture optimization, multi-scale data enhancement, dynamic parameter adjustment and efficient quantization deployment, the visual effect and the calculation efficiency are considered while the image brightness is improved.

The invention belongs to the technical field of image processing, and discloses a hyperspectral image classification method based on multi-scale space and spectrum enhancement fusion, which comprises the following steps: carrying out dimension reduction processing on a hyperspectral image, then generating a series of image blocks, and carrying out feature extraction on the image blocks to obtain shallow layer features; the shallow layer features are input into a multi-scale spatial-spectral enhancement module, features are extracted through spectral branches and spatial branches, and spatial-spectral enhancement fusion features are output after final fusion; performing image block embedding processing on the spatial-spectral enhancement fusion features to obtain image block feature vectors, and sending the image block feature vectors into a context semantic association module for semantic enhancement coding to obtain semantic enhancement features; and the semantic enhancement features are mapped to a target category after layer normalization and full connection processing, and finally a classification thermodynamic diagram is generated. According to the method, the inter-class separability is improved, and the precise perception and enhanced expression ability of fine ground feature features can still be kept in a complex scene.