254 results about "Image degradation" patented technology

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.

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 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 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 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.

The invention provides a multi-scene image degradation integrated recovery method based on a visual language model, which realizes high-quality image reconstruction under various weather degradation conditions, and comprises the following steps: inputting a given degraded image into an encoder for encoding to obtain encoding features; querying the pre-trained visual language model through a cross-modal prompt generator to generate a multi-scale degradation perception cross-modal prompt; the pre-trained visual language model takes a problem and a degraded image as input; the degeneration perception cross-modal prompt is input into the restoration trunk through a guide attention alignment module to be aligned and fused with the coding features, and final output of the guide attention alignment module is obtained; refining and fusing the degraded image and the final output of the attention alignment guiding module through a double feature compensation module to obtain the final total output; and carrying out decoding processing and image reconstruction on the final total output through a decoder to obtain a high-quality output image.

The invention discloses a CT (Computed Tomography) single-frame and multi-frame super-resolution fusion image enhancement method. The method comprises the following steps: S1, establishing a projection number-image effect model; s2, establishing a noise joint projection sheet number-image effect model; s3, degrading the CT image set based on the constructed projection sheet number-image effect model and the noise combined projection sheet number-image effect model to obtain a degraded CT image set; and S4, constructing a CT image inversion model based on the convolutional neural network, training the CT image inversion model by using the CT image before degradation, the CT image after degradation and the adjacent cutting layer image after degradation as training data of the network, and performing image enhancement on the to-be-processed CT image based on the trained CT image inversion model. According to the method, image noisy points can be effectively reduced, details in the image are enhanced, and the industrial CT slice image is converted into a high-quality clear image.

The invention discloses a printing-shooting process image degradation simulation method, device and equipment based on a graph-to-graph diffusion model, and aims to solve the problems that an existing degradation simulation method is greatly different from a real physical process and cannot be accurately controlled. The method comprises the following steps: firstly, constructing a printing-shooting data set containing a plurality of printing parameters and shooting parameters; then coding the physical parameters into conditional control vectors, and injecting the conditional control vectors into a Unet network of a graph-to-graph diffusion model for training; when an image is generated, an innovative double-flow noise layer structure is adopted, the structure combines a first branch adopting a traditional digital simulation method and a second branch adopting the pre-training diffusion model in parallel, and one of the first branch and the second branch is selected to be output according to a preset probability. According to the method, a highly vivid degraded image can be generated, so that the robustness and accuracy of downstream tasks (such as deep watermarking and image recognition) in a real printing-shooting scene are remarkably improved.

The application discloses a remote sensing image super-resolution reconstruction method based on spatial variable degradation perception, and comprises the following steps: S1, acquiring a high-resolution remote sensing image dataset; S2, constructing an image degradation model; S3, constructing a spatial variable degradation perception blind super-resolution model; S4, training a remote sensing image blind super-resolution model based on spatial variable degradation perception; and S5, reconstructing a high-resolution remote sensing image through the trained blind super-resolution model. The blind super-resolution reconstruction task is decomposed into a degradation process and a reconstruction process, on the one hand, the complex degradation process of an image is simulated, and on the other hand, the degradation conditions of different spatial positions of the image are considered, the degradation kernel closer to the real world is evaluated while the perception degradation range is expanded, serious kernel estimation deviation is avoided, and more accurate remote sensing image super-resolution reconstruction is realized.

The invention discloses a fan blade aerial image deblurring method fused with auxiliary image prior, which comprises the following steps of: establishing an image degradation model, estimating a blurring kernel and a potential image in the image degradation model by optimizing a target function, and after the blurring kernel is obtained, performing image deblurring on the image degradation model; non-blind deconvolution is applied to the input blurred image to generate a final clear image. According to the method, the auxiliary image is introduced in the process of estimating the blurring kernel, the structural consistency and defect identification degree of the recovered image are remarkably improved, a breakthrough from natural image priori to task specific priori is achieved, after the blurring kernel estimation is completed, standard deconvolution is not directly used, and the algorithm is simple and convenient to operate. Compared with the prior art, the non-blind deconvolution energy model fusing multiple image structure priori is innovatively proposed, the common problems of artifacts, edge blurring and the like in a traditional deconvolution image are greatly solved, high-fidelity image reconstruction is achieved, and the accuracy and robustness of a subsequent defect detection system are enhanced.

The invention provides a cold-rolled copper strip fog degradation image synthesis and restoration method, and belongs to the technical field of intelligent monitoring in the metallurgical rolling process, and the method comprises the following steps: collecting an original production image data set of a cold-rolled copper strip; constructing a cold-rolled copper strip fog degradation image data set; building an intelligent defogging network of the cold-rolled copper strip, and training and evaluating the network by using the data set to obtain an intelligent defogging model of the cold-rolled copper strip; and embedding the cold-rolled copper strip defogging model into an automatic visual inspection system in cold rolling production. According to the technical scheme, the cold-rolled copper strip fog degradation image data set with the paired data can be constructed, and the problem that image pairs are deficient due to real data is effectively solved. And secondly, aiming at the research blank of the fog degraded image restoration problem in the cold rolling production, the intelligent defogging model established by the invention can effectively relieve the image degradation problem caused by fog in the automatic visual inspection system in the cold rolling production, and has practical significance for improving the use performance of the automatic visual inspection system in the rolling field.

The invention relates to the technical field of computer vision, solves the technical problem that an image captured by current computer vision is easy to be influenced by the environment to cause image degradation, and particularly relates to a defogging and recognition combined network and a target detection method thereof, and features extracted from a target recognition network are shared to an image restoration network. And image restoration processing is carried out through the UNet structure. Clean features generated by the image restoration network are shared to the target recognition network. According to the method, the image restoration network based on the diffusion probability model is introduced, the target recognition network of the YOLOX model and the feature information provided by the feature enhancement module are matched, the defogging restoration task of the image is better completed, two deformable convolutions are introduced, the sensory domain of each convolution layer is effectively expanded, the output features are enriched, and the image defogging restoration efficiency is improved. Therefore, the influence of the specific weather information on the detection precision is reduced, and the defogging and target detection accuracy is good.