127 results about "Rain removal" patented technology

The invention relates to the technical field of image processing, and provides an image rain removal method based on space-frequency double-domain perception, which comprises the following steps of: 1, constructing an image rain removal network based on space-frequency double-domain perception; 2, training the image rain removal network model in the step 1 until the model converges; and step 3, for any acquired rainy day scene image, reasoning by using the rain removal network trained in the step 2 to obtain a rain-free background image. Compared with the prior art, the rain-removed image generated by the method has fewer rain stripe residues and clearer texture features, and the problems of rain stripe residues, blurred background and the like in an existing image rain-removing method can be effectively solved.

The embodiment of the invention provides an image rain removal method based on a multi-scale state space model, and belongs to the field of computer vision and image processing. The method comprises the following steps: acquiring an image with rain stripes, and decomposing the image into a multi-scale pyramid image through down-sampling; respectively inputting the image with the rain stripes and the multi-scale pyramid image into a pre-constructed multi-scale state space model, and performing feature extraction and image reconstruction through a convolutional layer and an encoder-decoder network to obtain a reconstructed residual image; and adding the image with the rain stripes to obtain a target rain-removed image. According to the method, global information is modeled by using a state space model, multi-scale complementary information is effectively utilized in combination with a multi-scale framework, cross-scale complementary features are explicitly mined, rain stripes are more accurately removed, a high-quality clear image is gradually recovered from a rain image, and the definition and visual quality of the rain-removed image are ensured.

The invention discloses a multi-scale Transform image rain removal method based on residual channel prior guidance, and the method comprises the steps: obtaining a rain image, inputting the rain image into a trained image rain removal processing network, and obtaining a rain-removed image. The training process of the image rain removal processing network comprises the following steps: acquiring a rain image sample and a corresponding real rain-free image; extracting a prior feature map of the rain image sample by using a residual channel prior extraction module; extracting features of the rain image sample by using a context feature extraction module to obtain shallow layer features of rain fringes on different scales; performing feature fusion to obtain a first comprehensive feature map; processing the first comprehensive feature map by using L recursive Transform extraction modules (RSTMs) to obtain an image after rain removal; and calculating the loss between the image after rain removal and a real rain-free image corresponding to the input rain image sample, and optimizing network parameters through back propagation of the loss. According to the invention, rain stripes can be removed from different directions and scales, so that the rain-removed image is more consistent with a real 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 relates to the technical field of image processing, and discloses a multi-domain interactive image rain removal method based on a state space model, and the method comprises the steps: processing a raindrop image through a rain removal model, and obtaining a restoration image after raindrop removal; the rain removal model construction and training process comprises the following steps: carrying out feature extraction on an input raindrop image to obtain potential features; performing multi-domain interaction processing on the potential feature F0 by using a FreqSSM module, and integrating and interacting information of different domains; after down-sampling the raindrop image, using an attention module to extract rain streak information, and fusing the rain streak information with the trunk information flow output by the FreqSSM module to obtain a fusion feature; the fusion features are fused with the raindrop image after passing through a recovery module formed by a convolutional layer, and a final recovery image is obtained; according to the invention, accurate identification and removal of raindrops in the image are realized through hierarchical space and frequency information interaction, and the naturalness and accuracy of the rain removal effect are improved.

The invention relates to the technical field of image rain and snow removal, in particular to a feature-driven GAN-based self-adaptive snow and rain removal method, which comprises the following steps: S1, constructing a feature extraction network architecture oriented to complex degradation features; s2, a spatial self-adaptive layered constraint loss system is established; s3, establishing a double-path reasoning framework of multi-stage collaborative optimization; and S4, constructing an industrial-grade credible evaluation system. The rain / snow removal frame based on the generative adversarial network is innovated in the aspects of generator architecture, loss function design and cooperative training of the generator and the discriminator, and by introducing a double-generator structure, an attention mechanism and space guide loss, the recovery performance of the rain and snow degraded image is remarkably improved; an effective solution is provided for practical application, and through the innovations, the model shows an excellent image recovery effect in a complex rain and snow scene.

The invention discloses an image rain removal method based on dual-channel fusion. The method comprises the following steps: establishing a data set containing an original rain image and a corresponding rain-free clear image; constructing a dual-channel fusion network for image rain removal; using loss, edge loss and frequency loss to form a mixed loss function for quantifying the difference between the rain-removed image and the rain-free clear image; training the dual-channel fusion network by adopting the data set; and the trained dual-channel fusion network is adopted to test a rain image to be subjected to rain removal, and a clear image after rain removal is obtained. According to the invention, a dual-channel fusion attention enhancement module, a mixed scale gating feedforward network module and the like are introduced into the model, so that the robustness and the feature extraction capability of the model are improved, and the model can better adapt to raindrop interference in different actual scenes.

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 belongs to the technical field of image processing, and particularly relates to a traffic image rain removal method based on a reference image, which comprises the following steps of: selecting and establishing a data set; preprocessing the data set; a rain removal model is constructed, and a backbone network extracts rich features by stacking a plurality of encoder modules, a multi-scale feature matching module and a decoder module; training the rain removal model by using the training set in the target data set, and obtaining a trained rain removal model after the trained rain removal model is verified to be qualified through the test set; and inputting a traffic rain map to be processed into the trained rain removal model for background image reconstruction, and outputting a reconstructed traffic road image by the rain removal model. The invention constructs a novel network model for natural image rain removal research, the network model focuses on feature extraction, and aims to accurately capture potential features in a rain map and a reference image corresponding to the rain map and ensure that more long-distance dependence information can be captured so as to enhance the restoration precision of a traffic background image.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

The invention relates to a two-stage image rain removal method based on a rain physical model and deep learning, and relates to the technical field of image rain removal. The method comprises the following steps: acquiring an original rain image; predicting according to the original rain image to obtain a corresponding atmospheric light image A, a transmissivity image T and a rain fringe image S; generating a plurality of rain-removed images Ji according to a rain physical model; performing weighted summation on the plurality of rain-removed images Ji by using a grouping weighting mechanism to obtain a preliminary rain-removed image J; and a PromptFormer is used to optimize the quality of the preliminary rain removal image J. According to the method, in the first stage, the rain removal reasoning logic is optimized in combination with a physical model of rain, in the second stage, a four-stage U-shaped network is introduced to optimize the output of the first stage, and in addition, the robustness of the whole algorithm is further enhanced in combination with a grouping weighting mechanism.

The invention discloses a multi-scale pulse gated image rain removal method and system with biological inspiration, and the method comprises the steps: firstly obtaining a synthetic data set of a rain removal task of a single image, and carrying out the preprocessing; secondly, a bionic driving color antagonism feature extraction mechanism is introduced, separation and enhanced expression of color degradation information in a rainy day image are achieved, and color antagonism features are obtained; then performing structure enhancement and multi-scale abstraction on the color antagonism features through bionic visual cortex multi-scale coding, and constructing visual cortex feature representation; and finally, for visual cortex feature representation, proposing a bionic visual cortex dual-channel prediction structure and a hybrid coding reconstruction structure, and outputting an image rain removal result graph. And constructing a multi-target mixed loss function, and carrying out reverse training and testing. According to the invention, the color information and the brightness information are accurately separated, the interference of background noise on the target color is reduced, and image rain removal is accurately and efficiently realized.

The invention discloses an image rain removal model, method and system, electronic equipment and a storage medium, and the image rain removal model employs a multi-output multi-scale architecture and has a plurality of scale branches operated under a plurality of resolutions; the encoder of each scale branch comprises a convolution layer which is used for receiving and extracting potential features of a to-be-processed rain image and obtaining a shallow feature map; the frequency feature enhancement unit is used for capturing multi-frequency information, acquiring context semantic information and acquiring a processing feature map; the multi-scale compensation Transform block is used for extracting global information and local detail features in each scale branch to obtain a deep feature map; a gating fusion module is arranged at the tail end of the encoder and is used for fusing depth features of all scale branches to obtain an enhanced feature map; a residual convolutional layer for outputting a residual image is arranged at the tail end of the decoder of each scale branch; the residual image is used for subtracting the received to-be-processed rain image to obtain a rain-removed image.

The invention belongs to the technical field of image processing, and relates to an image video rain removal method based on local perception modeling and multi-stage span fusion, which adopts parallel branch networks with three level scales to respectively and sequentially carry out feature extraction, feature extraction and multi-stage span fusion on raining images with corresponding resolution sizes, performing high-level semantic information mining on the input raining image to obtain a high-dimensional feature map; high-resolution image reconstruction: adopting a local set perception implicit representation module, mapping the high-dimensional feature map to a continuous RGB color space through linear coordinate projection, and compensating continuous textures and edges sheltered by rain in the image to obtain a reconstructed image; and cross-scale feature fusion: carrying out information fusion between stages and scales on the image through double branches, and aligning and enhancing multi-scale semantics to obtain a high-resolution rain-removed image. According to the method, artifacts of a raining image can be suppressed in a multi-domain and multi-scale level, and sharpness and consistency are enhanced.

The invention relates to an image rain removal processing method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps of establishing an unsupervised rain removal framework, performing reconstruction processing on an original rain image through an end-to-end image generation network to obtain a clear image, performing feature extraction on the original rain image through a rain image generation network to obtain raindrop information, obtaining a reconstructed clear image according to the raindrop information and the original rain image, and obtaining the reconstructed clear image according to the reconstructed clear image. The method comprises the steps of obtaining a clear image and raindrop information, obtaining a reconstructed rain image according to the clear image and the raindrop information, establishing a loss constraint condition for an unsupervised rain removal frame based on the original rain image, the clear image, the reconstructed clear image and the reconstructed rain image, and optimizing the unsupervised rain removal frame according to the loss constraint condition. And carrying out image rain removal processing on the original rain image through the optimized unsupervised rain removal framework. By adopting the method, the capability of removing local tiny rainwater can be improved, so that the rain removal of the image is accurately realized.

The invention relates to the technical field of image processing, and discloses an unmanned aerial vehicle video image rain removal method and device based on potential frequency domain characterization, and the method comprises the steps: carrying out the image comparison regularization processing of a complex dynamic video image of an unmanned aerial vehicle in rainy days, and obtaining a contrast regularization image; according to the contrast regularized image, guiding a pre-constructed potential frequency domain representation extraction network to extract potential frequency domain representation of the complex dynamic video image of the unmanned aerial vehicle in rainy days; performing dual-frequency potential characterization enhancement on the potential frequency domain characterization to obtain enhanced characterization; and performing representation aggregation image restoration according to the enhanced representation to obtain a rain-removed image. According to the unmanned aerial vehicle video image rain removal method, the image definition of the unmanned aerial vehicle in a complex dynamic environment in rainy days can be effectively improved, the robust and efficient video image rain removal effect of the unmanned aerial vehicle in the complex dynamic scene in rainy days is achieved, the visual effect of the image is finally improved, and a more accurate rain removal image is obtained.

The invention discloses an image rain removal method and device, which are used for solving the technical problem of poor image rain removal effect caused by difficulty in accurately balancing rain line removal and background detail recovery in the existing rain removal technology. The method comprises the following steps: acquiring a rain image, performing multi-scale down-sampling on the rain image, and outputting a multi-scale rain image; inputting the image with rain and the multi-scale image with rain into a preset multi-scale dual-domain collaborative rain removal network, wherein the preset multi-scale dual-domain collaborative rain removal network comprises an encoder, a multi-scale space interaction feature fusion module and a decoder; encoding the rain image and the multi-scale rain image by using an encoder, and outputting a plurality of target rain encoding features; carrying out feature fusion on the multiple target rain coding features through a multi-scale space interaction feature fusion module, and outputting target fusion features; and inputting the image with rain, the multi-scale image with rain, the target fusion feature and the target coding feature with rain into a decoder for decoding to generate a target rain-removed image.

The invention provides a low-illumination rainy day image enhancement method based on DEPMD-Net. Through heterogeneous double teachers and multi-stage distillation, polarized rain-light attention and implicit representation are introduced, separation and fusion of raindrop high-frequency information and illumination low-frequency information are realized, and an image with balanced brightness and without rain interference is generated. The method comprises the following steps: 1) synthesizing low-illumination rainy day data based on Rain100L and LOL-v1; 2) pre-training a brightness expert and a rain removal expert, and obtaining prior through bidirectional KL mutual learning; 3) utilizing brightness-rain mask statistics to construct joint priori, and adopting non-zero mean anchoring to simulate night rain distribution in diffusion noise scheduling; 4) proposing PiDNet-X to recover features through cooperation of a double-branch structure and rain-light attention, 5) performing staged distillation through a soft label and feature prompt, obtaining lightweight Student-C / F through a Born-Again strategy, and realizing night rain scene self-correction by double students in a reasoning stage through moving average and mutual learning.

The invention discloses an image processing method fusing frequency perception and a Mama network, and the method comprises the steps: collecting original data, carrying out the preprocessing, obtaining a sample set, training a constructed deep learning network, processing an input image in real time through the trained deep learning network at an online stage, and achieving a rainwater removal effect. On the basis of a fusion frequency perception and Mama network architecture, on one hand, discrete wavelet transform down-sampling operation, convolution operation, two wavelet domain recovery module feature extraction operation, convolution operation, two wavelet domain recovery module feature extraction operation, convolution operation and discrete wavelet inverse transform operation are sequentially carried out on a raindrop picture; and on the other hand, a high-frequency component is extracted from frequency information after wavelet transformation, and after frequency enhancement, a wavelet domain recovery module is sequentially guided to perform refined texture reconstruction, so that the rain removal effect is improved, and an image restoration result with higher quality is obtained.

The present invention relates to an image raindrop removal method based on the guidance of the reverse rain removal process. The method comprises the following steps: step S1, performing data preprocessing, including data pairing, data random cropping, and data enhancement processing, to obtain a training data set; step S2, designing a multiple attention module, using pixel attention, channel attention, and spatial attention to extract image features; step S3, designing a U-shaped encoder-decoder network based on the multiple attention module, which is used to extract features of the image at different scales; step S4, designing an image feature fusion module, which fuses the image features and the original image to obtain a fused restored image; step S5, designing a forward raindrop removal network and a reverse raindrop removal network, and using the reverse raindrop removal network to guide the training of the forward raindrop removal network; step S6, inputting the attached raindrop image into the trained forward raindrop removal network, and outputting a clean image with raindrops removed.

The present invention provides a method, system and storage medium for removing rain from images based on an improved KPN network. The method includes the following steps: inputting a preprocessed image into a feature extraction network layer to obtain an output image after feature extraction; inputting the output image after feature extraction into a common convolutional network layer that introduces a spatial attention mechanism to obtain a prediction kernel; inputting the obtained prediction kernel into convolutional network layers of different sizes to obtain prediction images of different receptive field features; weighted summing the features of each obtained prediction image, and inputting them into a deep separable convolutional layer to obtain a derained enhanced image. The present invention implements image deraining processing based on an improved KPN network image deraining method, introduces deep separable convolution, and greatly reduces the number of parameters and computational complexity of convolutional neural networks; introduces a spatial attention mechanism to improve the feature expression of key areas of feature maps, and effectively improves the effect of image enhancement.

The invention discloses an image rain removal method and device for unmanned aerial vehicle power grid inspection, and belongs to the technical field of unmanned aerial vehicle aerial image rain removal. The image rain removal method provided by the invention comprises the following steps: acquiring an aerial image acquired by an unmanned aerial vehicle, and performing step-by-step information mining operation on the aerial image to obtain a de-noised sub-image; the at least one level of information mining operation process comprises a spatial dimension reduction operation and a spatial dimension expansion operation which are performed in sequence; and based on the de-noised sub-image, constructing a target image after rain stripe removal, thereby completing image rain removal operation. In the feature information mining process of the aerial image, the spatial dimension reduction operation is firstly carried out, the content of rain stripe noise is greatly compressed, meanwhile, the influence on other objects in the image is relatively small, then the spatial dimension expansion operation is carried out, the sparsity and the dispersion degree of residual rain stripe information are improved, rain stripes are efficiently removed, and meanwhile the feature information mining efficiency is improved. And other image features are well reserved.

The invention relates to the technical field of image processing, and provides an image rain removal method and system based on multi-scale prior injection, and the method comprises the steps: carrying out the subtraction of a gray image formed by a maximum pixel value and a minimum pixel value of a rainy day image along a channel dimension; extracting multi-scale potential background prior features of the initial rough rain-free image through a residual branch of a pre-trained U-Net network; under the guidance of the multi-scale potential background prior features, multi-scale rain stripe features of the rainy day image are extracted through a PITform branch, and first-scale decoder rain stripe features are obtained; and the image passes through a Tform module and a convolution module and then is added with a rainy day image to obtain a rain-removed image. According to the method, the U-Net network is adopted, the prior information is introduced into each scale layer, the network is guided to generate the rain-free image with rich texture details, accurate image recovery is realized, and the rain removal performance is improved.

The application discloses a kind of based on image rain-removing method of generative adversarial network and convolutional neural network, comprising: according to rain-free image, utilize random noise to synthesize rainy image;The pre-processing of synthesized rainy image, and the multiple rainy images after pre-processing constitute data set;The generator and discriminator in the generative adversarial network GAN are trained using the data set, and the rain-removed image generated using the generative adversarial network model is used to train convolutional neural network CNN;Generative adversarial network GAN and convolutional neural network CNN are alternately trained until reaching preset condition, and trained generative adversarial network GAN and trained convolutional neural network CNN are obtained;The image to be removed is input into the generator in trained generative adversarial network and is removed, to obtain the image after rain-removing;The image after rain-removing is input into trained convolutional neural network, to obtain the rain-removed image after final optimization.

The invention relates to the field of deep learning and image processing, and discloses an image rain removal method based on wavelet transform and digital filtering detail guidance, which comprises the step of processing an input rain image by applying a trained image rain removal model, and comprises the following steps of: obtaining the rain image, and inputting the rain image into the image rain removal model for processing, and then outputting a rain removal image. According to the method, different image rain removal tasks can be better adapted by introducing image priori knowledge, image noise is effectively suppressed in combination with digital filtering, so that the quality and precision of recovery are improved, and the accuracy and robustness of image rain removal are improved by fusing feature information of different scales.

The invention relates to the technical field of electric power scene image restoration, and discloses a novel layer normalization method, system and device for an electric power scene image restoration model, and a medium, and the method comprises the steps: obtaining a target electric power scene image for the electric power scene image restoration model, and extracting the image features of the target electric power scene image; calculating spatial channel overall statistical information of the image features; calculating a normalized parameter according to the overall statistical information of the space channel; generating an overall normalization model of the space channel of the image features according to the normalization parameters; and performing layer normalization on the power scene image input into the power scene image restoration model in real time according to the overall normalization model. The method is suitable for various image restoration tasks such as image super-resolution, denoising, JPEG compression artifact removal and rain removal, can be directly applied to an inspection system to remarkably improve the image quality and availability, and provides high-quality input for subsequent defect recognition and toughness analysis.

The present application relates to a rainy day image fault-tolerant model based on an improved convolutional long short-term memory network, comprising: a feature extraction module and a regional feature enhancement module; the feature extraction module is used to extract rain streak features in rainy day images and efficiently learn key areas of interest, outputting a feature map; the regional feature enhancement module captures and removes raindrop information of different scales based on the feature map, and outputs a rain-removed image; the present application can capture rain streaks of different scales, while removing basic rain lines, protecting important image details, textures, and background information, thereby obtaining a rain-free image with higher color saturation and quality, making the restored image cleaner and having better visual effects.

The application discloses a parallel single image rain removal method based on a residual prior attention mechanism, and comprises the following steps: 1) obtaining an image to be removed; 2) extracting picture detail feature information through an image detail extraction network; 3) extracting picture rain line feature information through a rain line feature extraction network; 4) fusing the detail feature information and the rain line feature information through a feature fusion network; and 5) completing rain removal. The application strengthens effective feature information and weakens irrelevant items through a residual prior attention module, thereby improving the rain removal performance of the model; and the rain removal performance of the model is improved through reasonable fusion and fine-tuning of the detail feature and the rain line feature.

The application discloses a low-illumination rain-added image enhancement method based on a PERM-Net (Physics-guided Event-driven Rain Modulation Network), and proposes a joint enhancement network integrating degradation prior, event-driven coding and state space recovery in view of the problems of serious image brightness attenuation, obvious continuous rain streak interference, texture detail loss and insufficient perception ability of the enhanced result. The method comprises the following steps: 1) improving an existing data set; 2) constructing a degradation prior estimation module to jointly model the brightness attenuation, rain streak density and direction structure information in the image; 3) designing an event rain streak coding module to dynamically code the continuous rain streak features and extract the direction structure; 4) constructing a physics-guided rain streak Mamba block to realize image brightness recovery, rain streak suppression and global structure reconstruction; and 5) combining an adaptive fusion module and a perception optimization head to improve the detail expression ability of the enhanced image. The method realizes excellent rain removal effect and can generate an image rich in details and visually realistic in a complex environment.

The present application relates to a kind of two-stage image rain removal methods based on rain physics model and deep learning, it is related to image rain removal technical field.The method includes: collecting original rain image;According to original rain image, corresponding atmospheric light graph A, transmissivity graph T and rain stripe graph S are predicted;Multiple rain removal images are generated following rain physics model;The weighted summation of multiple rain removal images is obtained by applying grouping weighting mechanism, and preliminary rain removal image J is obtained;The quality of preliminary rain removal image J is optimized by applying PromptFormer.The method, in the first stage, combines the rain physics model to optimize rain removal inference logic, in the second stage, a four-stage U-shaped network is introduced to optimize the output of the first stage, in addition, the robustness of the entire algorithm is further enhanced by combining grouping weighting mechanism.