294 results about "Image degradation" patented technology

The invention provides a method for improving the image recognition precision of a new energy station, and relates to the technical field of image recognition, and the method comprises the steps: obtaining monitoring image data and environment parameter data, and carrying out the image preprocessing based on an environment impact factor and an image degradation score; extracting image features by using a deep convolutional neural network, and extracting time sequence correlation features by using a recurrent neural network; and feature optimization is realized through feature stability evaluation and dynamic calibration, and finally the working state and abnormal early warning information of the equipment are output. According to the method, the precision and robustness of new energy station image recognition can be effectively improved, and the interference of environmental factors on a recognition result is reduced.

The invention provides a low-light image enhancement method and device based on infrared visible light information integration, and belongs to the technical field of image enhancement, and the method comprises the steps: carrying out the processing of a normal illumination image through a series of image degradation methods based on image augmentation, and obtaining a pseudo low-light image needed by training; enabling the pseudo low-light image and the corresponding infrared image to pass through a submerged space and a multi-layer encoder to obtain a deep multi-scale feature, and building a multi-modal information interaction module based on sparse cross attention to process a dual-modal variable-scale feature and enhance feature interaction fusion; and a dual-mode residual feature enhancement diffusion model is established, and the feature enhancement diffusion model comprises a feature splicing module and a series of multi-mode information interaction modules, so that complementary features are learned and fused in the denoising process, the features are mapped to a normal exposure domain, and deep integration of image information is realized. According to the method, low-light images under various exposure conditions can be effectively enhanced, and the robustness is high.

The invention discloses an enhanced photoacoustic microscopic imaging method based on physical degradation learning, which comprises the following steps of: on the basis of a photoacoustic imaging principle, degrading an image of a real photoacoustic microscopic image to the ground into a low-quality photoacoustic microscopic image through physical model degradation learning with variability. A high-quality photoacoustic microscopic imaging image is used as a target of supervised training, deep learning is used as a basis, the high-quality photoacoustic microscopic imaging image is input into a self-designed convolutional neural network with an attention mechanism for network training, the quality of a reconstructed image is evaluated until the reconstruction quality reaches the standard, and otherwise, the above steps are executed again. And inputting a real low-quality photoacoustic image into the trained neural network to obtain a reconstructed high-resolution photoacoustic image. According to the method, the resolution ratio and the contrast ratio of the deep photoacoustic microscopic image can be effectively improved, meanwhile, the photoacoustic microscopic image can be denoised, the dependence on the data set can be effectively reduced due to the fact that the data set is obtained through the physical model, and the reconstruction effect under different environments can be improved.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention provides a ship target detection method based on nonlinear network enhancement, a medium and equipment, and belongs to the field of artificial intelligence. The method comprises the following steps of: performing type conversion, data division and enhancement on image data in a data set by adopting the ship data set acquired in a real river channel scene; a nonlinear network enhanced YOLO ship detection model is constructed; adopting a multi-task joint loss function to train the YOLO ship detection model; and inputting a port monitoring image and a sea surface aerial image into the trained YOLO ship detection model, outputting a category number, a confidence value and bounding box coordinates of each prediction box, and forming a visual image. According to the method, a nonlinear network enhanced YOLO ship detection model is constructed, and the modeling expression capability of the network on a ship target in a complex scene is effectively improved, so that the robustness and the accuracy of target detection are enhanced, and particularly, the performance is better under the conditions of small target detection and image degradation.

The invention discloses a vertical rail scanning remote sensing satellite natural multi-coverage image resolution improving method and system, and relates to the field of remote sensing image processing. The method solves the problems that due to the fact that the distance between an existing vertical rail scanning imaging sensor and a ground target is increased, image pixels cover a wider ground range, the actually-measured spatial resolution is reduced, and fine detection and recognition of ships, aircrafts and the ground target cannot be supported. SIFT feature point extraction is carried out, and feature points extracted from different images are matched by using a Euclidean nearest distance matching strategy; taking one registered image as a reference, and constructing an initial high-resolution image through up-sampling; establishing an imaging degradation model from a high-resolution image to a low-resolution observation image; calculating a residual image of the simulation image and the real observation image; and back-projecting the residual error back to the high-resolution image space, and updating high-resolution image estimation.

The invention relates to the technical field of intelligent driving, and discloses a defogging embedded system and method for an intelligent driving vehicle-mounted camera, and the method comprises the steps: obtaining original image data collected by the vehicle-mounted camera, and analyzing the brightness distribution, color shift and texture features in an image; based on the image degradation feature information, performing preliminary enhancement processing on the image by adopting an edge guided filtering and brightness adaptive mapping algorithm to generate an initial enhanced image; carrying out fog distribution estimation on the initial enhanced image, carrying out detail restoration and color compensation in combination with a lightweight image reconstruction strategy, and dynamically improving the image quality through an iterative optimization mode; performing definition evaluation on the optimized image, and measuring the fuzzy degree change trend and the structural similarity; and in combination with the finally output clear image, performing target identification and feature extraction operation, and performing accuracy evaluation on an identification result. The method has the advantage of improving the definition of the intelligent driving image.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The invention discloses an underground structure leakage intelligent detection system based on multi-modal image fusion and deep learning recognition. The system comprises an image acquisition and preprocessing module, a significance guide image fusion module, a bimodal target detection module and a feature fusion and output module. Compared with the prior art, the method has the following advantages: saliency guidance, channel attention and multi-modal joint training are combined, an information closed loop is constructed by a triple mechanism, and the image fusion quality is improved; bimodal parallel recognition and ANN fusion judgment are adopted to adapt to the image degradation condition in a complex environment, and the recognition accuracy of a weak signal area is improved; the system can be deployed in various underground structure scenes such as subways, tunnels, underground garages and pipe galleries, and is compatible with various hardware terminals; a lightweight feature extraction and rapid fusion module is provided, and the real-time processing requirement of edge computing nodes is met; a closed-loop detection system of image acquisition, fusion enhancement, depth identification and intelligent output is formed, the overall efficiency is high, and the false detection rate is low.

The invention discloses a real haze image defogging method based on a haze degradation model. The method comprises the following steps: constructing a haze degradation model fusing multiple scattering effects and multiple image degradation factors; using the haze degradation model to construct a training data set including the clear image and the corresponding pseudo haze image; constructing a defogging network for a real haze scene; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogging result. According to the haze degradation model constructed by the invention, the difference between a synthetic domain and a real domain is effectively relieved; a designed space-frequency hybrid module improves the adaptability of the model to complex degradation characteristics; the prior-guided feed-forward network fully excavates and fuses dark channel prior information, and the sensing and modeling capability of the model to the haze area is effectively enhanced.

The invention discloses a degraded image adversarial learning-based simulation embroidery design image reality evaluation method, and belongs to the technical field of digital printing. The method comprises the following steps: constructing an imitation embroidery design image adversarial learning evaluation model, wherein the model comprises a multi-scale degradation algorithm module, an embroidery design image generation sub-model and an embroidery design image evaluation sub-model; through image degradation, image reconstruction and region-by-region evaluation processes, design reality evaluation of different regions of the embroidery-imitating design image is realized in combination with an adversarial technology.

The invention provides an image defogging system and method for a low-altitude scene, and belongs to the technical field of image restoration based on computer vision. The method comprises the following steps: constructing based on a U-shaped network, guiding different frequency components to different layers by utilizing different sensitivities of the U-shaped network to frequency domain information, and respectively designing a frequency domain fusion module, a space and channel interaction module and a physical sensing module in a shallow layer, a middle layer and a deep layer; the frequency domain fusion module provides richer feature representation for the subsequent module, and the space and channel interaction module image of the middle layer captures important features of the space dimension and the channel dimension at the same time, so that the local definition and the global consistency of the defogged image are improved. A deep physical sensing module is combined with prior information of a physical model to provide stronger constraint and interpretability for a defogging task; according to the method, the problems of non-uniform fog distribution in a low-altitude scene and color distortion of a defogging result in a large-area haze region are solved, and the modeling capability of a network on image degradation characteristics is improved.

A wavefront coding and deep learning-based joint optimization image reconstruction method belongs to the technical field of image reconstruction, and aims to solve the problem of limited image reconstruction effect caused by independent processing of an image degradation process and an image reconstruction process in the prior art. The method comprises the following steps: building a decoding network model, preparing a data set, training a joint optimization network model, and testing an image reconstruction effect. Based on a collaborative optimization mechanism of a cubic phase coding plate and a deep neural network, wavefront coding parameters are introduced into a neural network training process by combining realizability of a cubic phase coding device, so that the wavefront coding parameters and network weight parameters are synchronously optimized. By guiding the input of the blurred image, more stable and consistent training input is provided for the deep learning network, and the convergence speed and generalization ability of the model are improved. And a decoding structure based on fusion of residual connection and an attention mechanism is adopted, so that the reconstruction precision and the visual quality are remarkably improved.

The invention discloses a substation inspection image optimization method and device, a terminal and a medium, and relates to the technical field of electric power system inspection. According to the scheme provided by the invention, a degradation type is judged through an image quality estimation module, and a degradation thermodynamic image corresponding to the degradation type is generated; introducing a thermodynamic image of an image degradation type as a dynamic space mask through a conditional causal attention mechanism, and constraining the sensing range of different degradation types in a cross attention mechanism, so that the model can focus on a local region related to specific degradation in a targeted manner, and feature interference among multiple degradation conditions is avoided; therefore, the image restoration model can effectively restore different degradation types, and finally, the enhanced high-quality image is input into the defect analysis module to obtain a more accurate defect identification condition, so that the aim of improving the accuracy and reliability of image monitoring in the power transformation station is fulfilled.

The invention discloses a three-dimensional scene reconstruction method based on camera luminosity optimization. According to the method, firstly, multiple images of the same scene are represented through a radiation field model, then a depth regularization method is introduced, points obviously deviating from the surface of an object are inhibited, the overfitting problem is relieved, and a projection image at a corresponding view angle is obtained. Secondly, an external luminosity model is established by considering the distortion of a camera lens caused by the influence of dust, stains, other interferents or atomization, and an internal luminosity model is established by considering the internal distortion problem influenced by halation and non-uniformity of a sensor; by optimizing the radiation field characterization parameters and the camera luminosity model parameters, the camera luminosity parameters can be estimated in the three-dimensional reconstruction process, so that compared with the prior art, the method can separate and obtain a high-quality three-dimensional scene with higher camera luminosity distortion and robustness under the condition of camera luminosity distortion, especially under the condition of imaging degradation, the three-dimensional scene with higher camera luminosity distortion and robustness can be obtained. And high-quality 3D scene representation can also be realized.

Systems are provided for dynamically splitting input images into a plurality of input tiles for processing by a super-resolution model. The size and halo region of the input tiles is based on a degradation associated with the convolutional operations of the super-resolution model. The input tiles are processed by the super-resolution model to generate output tiles that are stitched together into output images that are of a higher or different resolution than the input images.

The invention discloses a Gaussian sputtering general rendering enhancement method based on space fusion. The method comprises the following steps: obtaining a real image to be processed; constructing a degradation simulator of a Gaussian sputtering style, adding a gating mechanism into the degradation simulator, selecting a degradation type in an image degradation process, and generating a training data set; inputting a real image to be processed into the degradation simulator to generate a training image; and a Gaussian intensifier is constructed, the Gaussian intensifier utilizes a spatial information fusion network to carry out feature fusion and enhancement rendering processing on the training image through a view fusion module and a depth modulation module, and a final enhancement result is generated. According to the method, highly-correlated and high-quality training images can be mixed, and details are rendered by using the depth information of the target image, so that the output rendering quality of the image is improved.

The invention discloses an underwater image degradation diffusion self-supervised enhancement model and a building and training method, and relates to the technical field of underwater image enhancement. The invention provides an underwater image degradation diffusion self-supervised enhancement model and a building and training method for the problem of target detection performance reduction caused by underwater image quality degradation, the model built by the method is combined with the differential thought of a Koschmieder illumination scattering degradation model and a diffusion model, the degradation process of a real underwater environment is simulated, and the target detection performance is improved. Self-supervised training data is generated, dependence of a traditional method on manual annotation data is avoided, image features are extracted through a ResNet50 backbone network, global background light and transmission rate are predicted in parallel, a clear image is reversely recovered by using a physical formula, transmission rate consistency loss, background light consistency loss and reconstruction loss are also proposed, an unsupervised optimization target is constructed, and the self-supervised training data is obtained. And the accuracy and stability of model training are ensured.

The invention discloses a two-stage multi-task image restoration method based on an RDM-CS framework (see figure 1), which can process various image restoration tasks such as low illumination, rain removal, snow removal, defogging and the like at the same time. Comprising the following steps: 1) processing and dividing a data set; and 2) carrying out feature extraction by using a VQGAN encoder and completing forward diffusion on the feature zt. And 3) training the prediction noise network and the prediction residual network to obtain residual and noise, and then completing reverse generation. And (4) training a CS (Channel-Spatial Transform) network (Channel-Spatial Transform). And 5) testing in the test set of each data set, and evaluating the image restoration effect. According to the method, through the multi-stage feature extraction and diffusion process, the model can effectively process various image degradation problems, and the image restoration quality and efficiency are improved. The system has a unified model architecture, can adapt to different image recovery tasks, and has relatively high practicability and popularization value.

The invention discloses a Mars high-resolution image functional analysis stripe removing method and system and terminal equipment, and relates to the technical field of planet remote sensing image processing. The method comprises the steps of receiving Mars high-resolution image data containing stripe noise as original data input, and performing preprocessing; a high-resolution image degradation model is constructed, wavelet decomposition is carried out on original noisy image data based on a wavelet transform method, the number of layers of wavelet decomposition is n, and the wavelet decomposition is stopped until no noise appears in each sub-image after decomposition. The method combines the advantages and characteristics of multi-resolution of wavelet transform and automation in energy functional analysis, combines the advantages of the two methods, performs multilayer wavelet decomposition on an original image, and then operates sub-images of corresponding layers, so that strip noise is removed to the greatest extent, and space details of ground features are reserved to the greatest extent; and the fidelity of the denoised image is high.

The invention provides an image model construction method and system based on random bright spots and irregular stripes, and relates to the technical field of image processing. The method comprises the following steps: acquiring a data set in an image region, observing and analyzing the distribution characteristics of the hot spots in size, intensity and position, establishing a mathematical model of random hot spots, analyzing the complex structure characteristics of irregular stripes in an image, simulating the superposed spatial noise, and carrying out detailed modeling on various background thermal noises which influence the image quality, so as to obtain a random hot spot model. According to the method, common degradation factors in the image processing process are considered, accurate modeling is carried out, obtained different types of noise models are integrated to form a comprehensive high-order thermal infrared image degradation model, various degradation phenomena experienced by the thermal infrared image in the acquisition, transmission and processing processes are comprehensively simulated, the image processing efficiency is improved, and the image quality is improved. And the picture resolution is improved.

The utility model provides a refrigeration type infrared detector and a thermal imager, the refrigeration type infrared detector comprises a Dewar assembly and a refrigerating machine, the refrigerating machine comprises an expansion machine and a compressor, the expansion machine and the Dewar assembly are coaxially connected, the expansion machine and the compressor are communicated through a connecting pipe, and the Dewar assembly and the compressor are coaxially connected. The compressor is arranged on the side, away from the Dewar assembly, of the expansion machine, and the axis of the compressor is perpendicular to the axis of the Dewar assembly. According to the refrigeration type infrared detector and the thermal imager, the structural layout is reasonable, the heat dissipation effect of the refrigerator is enhanced, and the image degradation phenomenon caused by the temperature rise of the shutter can be effectively improved.

The embodiment of the invention provides a model training and image processing method and device, a storage medium and a program product. In the embodiment of the invention, the target degradation parameter is obtained by optimizing the initial degradation parameter with the target that the texture loss after image degradation processing is smaller than or equal to the set texture loss threshold value, so that the target degradation parameter is obtained according to the target degradation parameter obtained through optimization. The texture loss of the input image of the model obtained by carrying out degradation processing on the target image is smaller than the texture loss threshold compared with the target image, so that a moderately degraded training sample is generated, and the degraded image still keeps some texture information to provide reliable context clues for the model; and texture reasoning can be carried out based on real observation instead of generating some artifacts, so that texture enhancement is effectively realized.

The invention relates to the technical field of image processing, and discloses a low-quality chromosome image segmentation enhancement system, which extracts features and perceives degradation through a multi-scale degradation perception coding module, separates a structure from noise through a frequency domain feature intelligent decoupling module, and generates an enhanced image and a segmentation mask through a joint decoding and contour enhancement module. And finally, the whole system is optimized through a composite loss function by an adversarial discrimination and loss optimization module, so that high-quality enhancement and accurate segmentation of a low-quality chromosome image are realized. By accurately sensing and separating image degradation information, specially enhancing the continuity and definition of chromosome contours, adopting an end-to-end optimization framework which is tightly coupled with enhancement and segmentation tasks, and introducing an antagonism discrimination mechanism containing various targeted losses, the ability of extracting pure structural features from low-quality images is significantly improved, and the method has the advantages of being high in robustness and high in robustness. Contour defects are effectively repaired, the overall performance is optimized, and an enhanced image and a segmentation mask with high reality and high precision are generated.

The invention discloses a method for restoring flame shielding imaging based on a large model and a polarization frequency domain network, and the method is characterized in that the method comprises the following steps: 1, building a flame shielding target imaging system, and shooting a clear image of a target object without flame shielding; respectively shooting polarization images of the target object under the conditions of low flame shielding rate, high flame shielding rate and high-intensity flame shielding, and obtaining a low flame shielding rate data set, a high flame shielding rate data set and a high-intensity flame shielding rate data set; according to the restoration method provided by the invention, the problem of serious image degradation caused by strong radiation of flame and scattering of internal particles is effectively solved, and clear imaging of a target behind non-uniform flame shielding is realized. The method is based on an innovative double-branch deep network, and a polarization frequency domain branch accurately separates and extracts submerged target detail information by utilizing the difference between a target and flame in polarization characteristics and combining frequency domain analysis.

The invention is suitable for the technical field of radar signal processing, and provides a sparse-neighborhood joint constraint-based deep expansion ISAR super-resolution imaging method, which comprises the steps of firstly constructing an ISAR image degradation model, constructing an ISAR super-resolution imaging problem based on the ISAR degradation model and a compressed sensing theory, and solving the ISAR super-resolution imaging problem by using an ADMM (Amplitude Division Multiplexing). The process of solving the ISAR super-resolution imaging problem by the ADMM is expanded into a multi-stage neural network, and finally, the low-resolution ISAR echo is input into the trained neural network to obtain an ISAR super-resolution imaging result. According to the method, the sparsity constraint and the neighborhood amplitude constraint are combined, the corresponding signal model and the ADMM solving algorithm are deduced, the reconstruction capability of the algorithm on complex target details and the representation capability on structural information are effectively improved, and effective super-resolution imaging can be realized based on narrow-band short-aperture echoes.

The invention relates to image processing, in particular to a complex environment-oriented power transmission line inspection image processing method, which comprises the following steps of: screening key frames with high geometric information content by quantitatively evaluating inter-frame motion amplitude and feature tracking quality; extracting features of each key frame by using a pre-trained visual model; performing intra-frame feature aggregation on the features of each key frame by using an intra-frame self-attention mechanism to obtain corresponding intra-frame feature representation; performing global feature aggregation on the intra-frame feature representations of all the key frames by using a global self-attention layer to obtain inter-frame feature representations; constructing a multi-task learning network, and carrying out end-to-end collaborative optimization on depth estimation, image defogging and high-level semantic segmentation; inputting the inter-frame feature representation into a multi-task learning network, and obtaining a restored clear image through deep fusion of scene depth information and image degradation priori; according to the method, the defect that the dual requirements of quality improvement and feature retention of the power transmission line inspection image in a complex environment cannot be met can be overcome.

The invention relates to the technical field of image processing, and discloses an image deblurring method, which comprises the following steps of: performing fuzzy detection and classification: constructing a fuzzy image classification module by adopting a fuzzy recognition mechanism based on dark channel prior, generating an auxiliary channel with fuzzy perception capability by an input image through a dark channel feature extraction module, and performing fuzzy detection and classification on the auxiliary channel; and then the original image and the dark channel image are spliced and then input to a fuzzy recognition module, whether the image is obviously fuzzy or not is quickly judged, and redundant processing on the clear image is avoided. Through the structural design of a multi-stage multi-task joint model, the problem that the performance and efficiency in the vehicle-mounted image deblurring and super-resolution reconstruction tasks are difficult to consider at the same time is effectively solved, and meanwhile, the defects of traditional synthetic data in the aspects of fuzzy diversity and perception consistency are effectively overcome through a diversified data set construction method; the trained model can adapt to various image degradation conditions in an actual vehicle-mounted scene, and the robustness and adaptability of the model in the real scene are improved.

An example method includes capturing a scene by a camera system. The method includes displaying, by a display screen of the camera system, a live preview of a first portion of the scene. The method further includes receiving, by the display screen, a user indication to activate a locked preview at the display screen, wherein the locked preview comprises a perceived reduction of one or more image degradations associated with a motion of the camera system. The method also includes in response to the user indication, displaying the locked preview. The method additionally includes detecting, by the display screen, a user interaction with the locked preview. The method further includes generating a modified locked preview to show a second portion of the scene based on the user interaction. The method also includes displaying, by the display screen, the modified locked preview showing the second portion of the scene.