50results about How to "Improve the effect of super resolution" patented technology

The invention discloses a super-resolution image generation method. The super-resolution image generation method includes inputting a real image sample into a generation network so as to output a super-resolution image sample after the generation network and a distinguishing network are preset, acquiring distinguishing probabilities outputted by the distinguishing network after the real image sample and the super-resolution image sample are outputted, determining generation network loss functions and distinguishing network loss functions according to the real image sample, the super-resolution image sample and the distinguishing probabilities, and adjusting configuration parameters of the generation network and the distinguishing network according to the generation network loss functions and the distinguishing network loss functions; receiving a low-resolution image to be processed after adjustment is completed, generating a super-resolution image of the low-resolution image according to the generation network, and subjecting the super-resolution image to visualized processing. By the super-resolution image generation method, image super-resolution effect and realness of the super-resolution image are both improved remarkably.

The invention relates to an image super-resolution method based on a channel attention mechanism and multilayer feature fusion, and the method comprises the steps of directly extracting the original features of a low-resolution image at the beginning of a residual branch by using a single-layer convolutional layer based on deep learning; using six cascaded convolutional circulation units based ona channel attention mechanism and multi-layer feature fusion to extract accurate depth features; carrying out upsampling on the depth features through a deconvolution layer, and carrying out dimensionality reduction on the upsampled features through a single-layer convolution layer to obtain a residual error of the high-resolution image; carrying out up-sampling on the low-resolution image by using a bicubic interpolation method in a mapping branch to obtain mapping of the high-resolution image; and adding the mapping and the residual of the high-resolution image pixel by pixel to obtain a final high-resolution image. The method is reasonable in design, fully considers the difference between the feature channels, efficiently utilizes the hierarchical features, and maintains a higher operation speed while obtaining higher accuracy.

The invention discloses a video live broadcast method for wearable devices, comprising processing at a sender and processing at a receiver. The sender decreases the spatial resolution of a high-definition video signal four times and then executes standard video encoding so as to greatly reduce the pressure put on a wireless network by the encoding data, and the receiver restores the proper resolution through super-resolution reconstruction. Time-domain motion matching needed for super-resolution reconstruction is speeded up with the help of inherent motion vector information of a video encoder. Moreover, a reconstruction residual error compensation link is introduced into a video codec loop to eliminate distortion between a super-resolution high-definition image and a real high-definition image. The mobile video live broadcast efficiency of wearable devices is improved greatly under the premise of no additional damage to the quality of high-definition videos.

The invention relates to a fine-grained scale image super-resolution method based on a non-local enhancement network, and the method comprises the following steps: A, carrying out the preprocessing ofan original high-resolution training image, and obtaining an image block pair data set composed of low-quality high-resolution image blocks of different scales and original high-resolution training image blocks; B, training a non-local enhanced deep network for the data set by using the image blocks; C, inputting the high-resolution image of the low-quality test image into a deep network for reconstruction to obtain a super-resolution result. According to the method, a non-local enhanced deep residual structure is used, non-local operation and common convolution are combined, local and non-local image attributes can be effectively captured, image super-resolution is carried out, and compared with an existing super-resolution model, the method can remarkably improve the performance of image super-resolution on the fine-grained scale.

The invention discloses a single image super-resolution method based on a reversible network, and belongs to the field of image processing. According to the method, a network structure of a super-resolution model is constructed by introducing a reversible network; mutual mapping of a high-resolution image space and a low-resolution image space is realized by utilizing the reversible property of areversible network; the super-resolution process is optimized in the low-resolution direction and the high-resolution direction, the problem that other super-resolution methods based on deep learningcannot effectively utilize the mutual dependence between high-resolution images and low-resolution images is solved, and therefore the image super-resolution capability of the model is improved. A weight matrix of the 1 * 1 reversible convolution layer is initialized by introducing singular value decomposition, so that the propagation speed of the inverse process of the 1 * 1 reversible convolution layer is increased; by the adoption of the method, the super-resolution process of a single image can be effectively achieved, and the super-resolution image with good texture details and visual effects is generated through the low-resolution image.

The invention discloses an image super-resolution method based on a densely linked neural network, a storage medium and a terminal. The method includes: preprocessing an image; performing Feature extraction: Building a dense-linked neural network, inputting the low-resolution image Input from the entrance of the dense-linked neural network, and extracting the feature information contained in the Input after calculation; Predicting the super-resolution image and updating the network parameters: performing upsampling/deconvolution on the feature-extracted image to obtain the predicted image predict; calculating the error values between the predicted image predict and the real image label, and updating the parameters of the densely linked neural network in the reverse direction; and performing super resolution reconstruction. The method can remarkably improve the ability of extracting the low-frequency and high-frequency features of an image by a depth neural network, improve the effect of the image super-resolution, and improve the ability of providing information by a picture, so that the invention is applied in the field of expecting to obtain a high-resolution image and providingmore details by the picture.

The invention discloses a multi-frame video super-resolution method fused with an attention mechanism. The method comprises the following steps: collecting video data and training the video data by adopting a video enhancement technology to generate a training set and a test set; connecting the deformation convolution feature alignment module and the feature reconstruction module to form a multi-frame super-resolution network, and training the multi-frame super-resolution network by using the training set; adding a 3D convolution feature alignment module into the multi-frame super-resolution network, and training the multi-frame super-resolution network by adopting the training set; adding the feature fusion module into a multi-frame super-resolution network, and training the multi-frame super-resolution network by adopting the training set; finely adjusting the multi-frame super-resolution network by adopting the training set to generate a multi-frame super-resolution model; and testing the multi-frame super-resolution model by adopting the test set. According to the invention, the super-resolution effect can be effectively improved by analyzing big data.

The invention discloses an image restoration method based on an enhanced neural network. The method comprises the following steps: S1, converting an image to be restored into a plurality of low-resolution images under different zoom factors; S2, converting the image to be restored into a plurality of low-resolution images under different zoom factors; S3, converting the image to be restored into aplurality of low-resolution images. S2, respectively inputting a plurality of low-resolution images to a first depth convolution neural network trained in advance, thereby obtaining a plurality of high-resolution images under corresponding different scaling factors; S3, converting a plurality of high-resolution images in S2 into images having the same size as the image to be restored, and fusingthese images to obtain the restored images. The invention also discloses a corresponding storage medium and an image restoration system. The invention can prevent the network from being degraded in the training process and accelerate the convergence speed.

The invention discloses a single-frame image super-resolution reconstruction method based on cascade regression base learning. The method comprises the following steps: taking a super-resolution reconstruction technology of a single-frame low-resolution image as a research object, learning a multi-layer over-complete sub-dictionary for representing an image structure, constructing a mapping relation between a low-resolution image and a high-resolution image, and learning an optimized regression base and a corresponding coding coefficient; and then, complete super-resolution reconstruction is realized for the low-resolution image set, and the reconstructed image is used as a low-resolution image of the next layer for feature extraction. The invention discloses a single-frame image super-resolution reconstruction method. learning by utilizing a meta-dictionary learning method to obtain a low-resolution dictionary; a weighted linear regression method is used for carrying out multilayer regression base learning on a reconstructed high-resolution training set image and an original high-resolution image in a cascading mode so as to approach a complex nonlinear mapping relation between alow-resolution image and a high-resolution image, and instance regression super-resolution reconstruction with high processing speed, small memory occupation and high reconstruction quality is achieved.

The invention relates to the technical field of image processing, and provides an image super-resolution method and device, terminal equipment and a computer storage medium. The method comprises the following steps: extracting image features from a to-be-processed image, inputting the image features into a texture feature extraction module to extract texture features of the image, processing the texture features by adopting a plurality of convolution layers to obtain structural features of the image, fusing the texture features and the structural features of the image, and finally recovering the fused texture features and structural features into an up-sampled image; and obtaining a corresponding super-resolution image. Compared with a traditional image super-resolution method, the image super-resolution method has the advantages that richer image details can be obtained by fusing the texture features and the structural features of the image, and therefore the image super-resolution effect is improved.

The invention provides a video space-time super-resolution implementation method and device, and the method comprises the steps of carrying out the edge enhancement of a video frame of a video, and obtaining an edge-enhanced video frame; inputting the plurality of edge-enhanced adjacent video frames into an optical flow estimation module in pairs to obtain a bidirectional optical flow; calculating the bidirectional optical flow to obtain an estimated optical flow, and inputting the estimated optical flow and the bidirectional optical flow into a bidirectional prediction module together to obtain a predicted optical flow; calculating the prediction optical flow and the corresponding video frame to obtain an intermediate frame for time domain super-resolution, and inserting the intermediate frame into a corresponding position in the video; and performing spatial domain super-resolution processing on the intermediate frame and the corresponding video frame through a cyclic super-resolution network to obtain a plurality of reconstructed frame; and circularly executing the steps until the space-time super-resolution of the whole video is completed. The invention has the beneficial effects that space-time joint super-resolution can be effectively carried out on the video, and the visual quality of the video is improved.

The invention discloses a residual instance regression super-resolution reconstruction method based on multistage dictionary learning, and the method comprises the following steps: generating a training set through high-resolution images, and establishing block pairs of low-resolution and high-resolution image blocks; extracting feature vectors of low-resolution image blocks, and learning a dictionary with strong representation ability by using K-SVD as an anchor point; performing the least square regression of low-resolution and high-resolution blocks in the block pairs through the dictionaryobtained via learning, and obtaining a linear mapping relation; estimating the high-resolution features, calculating a reconstruction error, and carrying out the mapping of the estimated high-resolution features and the reconstruction error while the further dictionary learning of the estimated high-resolution features; obtaining a group of residual regression devices after the L layer; carryingout the reconstruction through an inputted image and the obtained residual regression devices, and enabling the obtained high resolution features to be used for the reconstruction of a next layer; adding all estimated high-resolution image blocks and forming a high-resolution image through synthesis. The method is stronger in super-resolution capability, and can be used for the amplification of alow-resolution natural image.

The invention discloses an image denoising method and device, electronic equipment and an image super-division denoising method. The image denoising method comprises the following steps: respectivelyacquiring a first noiseless image and a second noiseless image with different image contents; performing alternate iterative training on the initial noise generation network and the discrimination network by using the first image and the second image to obtain a newly-built noise generation network; inputting the first image into a newly-built noise generation network, and outputting a third imagewith analog noise; training the initial image denoising network by using the first image and the third image as paired training samples to obtain a trained newly-built image denoising network, wherein the newly-built image denoising network is configured to convert the original image with noise into a noiseless newly-built image. According to the image denoising method, the image denoising network used for converting the low-resolution image into the high-resolution image can be obtained by training the asymmetric training set, and the image super-resolution effect is improved.

The invention discloses a super-resolution image texture optimization method and device. The method is realized on the basis of time-frequency feature extraction and mapping kernel learning, and comprises the following steps: 1) training a local texture block sample screener on the basis of local texture blocks with time domain and fractional frequency domain features, and screening samples with obvious texture features from external samples to serve as training samples of a mapping kernel training module; (2) learning a texture block optimization mapping kernel based on a local texture block of external sample learning, and (3) carrying out texture optimization on a to-be-optimized picture. According to the method and the device provided by the invention, the texture of the super-resolution image can be clearer and more natural.

The invention discloses a light field image multi-magnification spatial super-resolution method and device. The method comprises the following steps: S1, training a single-image super-resolution network based on an EDSR structure; s2, using the trained single-image super-resolution network model to carry out single-image super-resolution with the magnification of m on all view angle images of the low-spatial-resolution light field image; s3, extracting a polar plane image before restoration from a single-image super-resolution result to form a polar plane image cube; s4, constructing a U-shaped polar plane image cube multi-magnification repair network based on an attention residual structure; s5, training the multi-magnification repair network of the polar plane image cube, repairing the information of the polar plane image cube by using the trained network model, and obtaining the polar plane image cube in which the geometric continuity of the space target in the visual angle dimension is recovered; and S6, reconstructing the repaired polar plane image cube into a light field image space super-resolution result with the multiplying power of m. According to the method provided by the invention, the super-resolution effect of the network is improved.

The invention discloses a face image super-resolution method based on an improved deep iterative collaborative network, and the method comprises the steps: 1), carrying out the early-stage data processing, and obtaining low-resolution face image data; 2) inputting a low-resolution face image into an image super-resolution sub-network, and processing the face image by a shallow feature extraction module to obtain shallow features; 3) the output of the shallow feature and prior information extraction sub-network in the last iteration process is sent to the image super-resolution sub-network to obtain high-resolution features, and a reconstruction module reconstructs the high-resolution features to obtain a high-resolution image; 4) inputting the high-resolution image into the prior information extraction sub-network, and outputting a face key point thermodynamic diagram, a semantic analysis diagram and intermediate layer features at the same time; and (5) repeating the steps (3) and (4), and carrying out iteration for N times to obtain final high-resolution image output. According to the method, the structure information of the face image is fully considered, the super-resolution effect of the face image is better, the parameter scale is smaller, and the time overhead is smaller.

The invention provides a distributed array SAR sparse representation three-dimensional imaging algorithm considering scene amplitude real value constraints. According to the algorithm, a sparse representation theory is adopted, a complex scene is represented as a combination of amplitude and phase, a scene amplitude real value constraint term is added into a regularized reconstruction model by using two pieces of priori information that the three-dimensional scene sparsity and the scene amplitude as real numbers, and the scene amplitude and phase are respectively estimated by using a quasi-Newton algorithm, thereby completing three-dimensional reconstruction more conforming to the actual situation. According to the method, amplitude real value constraints are added to the regularized reconstruction model, so that the measurement model is more reasonable, the actual situation is met, and high-resolution three-dimensional imaging with higher super-resolution capability and higher robustness can be realized.

The invention discloses an unsupervised image super-resolution fuzzy kernel estimation method and a terminal, and the method comprises the steps: obtaining an original input image, carrying out the augmentation of the original input image, and outputting a plurality of images; obtaining any image in the plurality of images, and performing down-sampling through an encoder to obtain a down-sampled image; according to the down-sampled image and the original input image, closing the block distribution between the down-sampled image and the original input image through a discriminator; performing up-sampling on the down-sampled image through a decoder to obtain the size of an original input image to obtain a reconstructed image; extracting blurred kernels of the plurality of images after the augmentation operation through an encoder, and obtaining a final blurred kernel after average processing. According to the method, the blurred kernel is estimated by learning the internal information ofthe image through the encoder, and the estimated blurred kernel is corrected through the feedback of the decoder, so that the accuracy of blurred kernel estimation is improved, and the super-resolution performance of the image in an unsupervised scene is improved.

The invention relates to a video processing method and device. The video processing method comprises the following steps: acquiring all video frames of a video; for each of the video frames, the following processing is performed: calculating a change image between a current video frame and an adjacent video frame, wherein the adjacent video frame is a video frame adjacent to the current video frame, calculating spatial attention information between the current video frame and the adjacent video frame based on the change image, obtaining the features for super-resolution of the current video frame based on spatial attention information between the current video frame and the adjacent video frame, and generating a high-resolution video frame for the current video frame based on the feature for super-resolution. According to the video processing method and device, the attention to the complementary region can be improved based on the spatial attention information, and the attention to the redundant region can be reduced so that the super-resolution performance is improved.