Abstract: A computer-implemented method using a convolutional neural network is provided. The computer-implemented method using a convolutional neural network includes processing an input image through at least one channel of the convolutional neural network to generate an output image including content features of the input image morphed with style features of a reference style image. The at least one channel includes a down-sampling segment, a densely connected segment, and an up-sampling segment sequentially connected together. Processing the input image through the at least one channel of the convolutional neural network includes processing an input signal through the down-sampling segment to generate a down-sampling segment output; processing the down-sampling segment output through the densely connected segment to generate a densely connected segment output; and processing the densely connected segment output through the up-sampling segment to generate an up-sampling segment output.

Abstract: An image processing method includes: obtaining an input image; performing image conversion processing on the input image by using a generative neural network; and outputting an output image that has been subjected to image conversion processing. The input image has N channels, N being a positive integer greater than or equal to 1; input of the generative neural network includes a noise image channel and N channels of the input image; output of the generative neural network is an output image including N channels.

Abstract: A neural network is provided. The neural network includes 2n number of sampling units sequentially connected; and a plurality of processing units. A respective one of the plurality of processing units is between two adjacent sampling units of the 2n number of sampling units. A first sampling unit to an n-th sample unit of the 2n number of sampling units are DeMux units. A respective one of the DeMux units is configured to rearrange pixels in a respective input image to the respective one of the DeMux units following a first scrambling rule to obtain a respective rearranged image. An (n+1)-th sample unit to a (2n)-th sample unit of the 2n number of sampling units are Mux units. A respective one of the Mux units is configured to combing respective m? number of input images to the respective one of the Mux units to obtain a respective combined image.

Abstract: A neural network is provided. The neural network includes 2n number of sampling units sequentially connected; and a plurality of processing units. A respective one of the plurality of processing units is between two adjacent sampling units of the 2n number of sampling units. A first sampling unit to an n-th sample unit of the 2n number of sampling units are DeMux units. A respective one of the DeMux units is configured to rearrange pixels in a respective input image to the respective one of the DeMux units following a first scrambling rule to obtain a respective rearranged image. An (n+1)-th sample unit to a (2n)-th sample unit of the 2n number of sampling units are Mux units. A respective one of the Mux units is configured to combine respective m? number of input images to the respective one of the Mux units to obtain a respective combined image.

Abstract: An image processing method implemented by a computing device is described herein, which includes acquiring an image to be processed and a target style of image, the image to be processed being an image of a second resolution level, and inputting the image to be processed and the target style into a trained image processing neural network for image processing to obtain a target image of the target style, the target image being an image of a first resolution level. The resolution of the image of the first resolution level is higher than that of the image of the second resolution level.

Abstract: A convolutional neural network, and a processing method for the same. The method includes: using an activation recorder layer as an activation function layer in the convolutional neural network, wherein in response to that a probe image with contents is inputted to the convolutional neural network, the activation recorder layer performs an activation operation the same as the activation function layer does and records an activation result of the activation operation; modifying the convolutional neural network, wherein step of modifying includes replacing the activation recorder layer with a hidden layer that uses the recorded activation result; and inputting an analysis image to the modified convolutional neural network as an input image, so as to output an output image of the modified convolutional neural network, thereby analyzing a forward effect or a backward effect between the input image and the output image, the analysis image being a pixel-level binary image.

Abstract: Provided are a processing method and system for a convolutional neural network, and a computer-readable medium, the processing method includes training a generator and training a discriminator, wherein training a generator includes: extracting a low-resolution color image from a high-resolution color image; training parameters of a generator network, by using the low-resolution color image and a noise image as an input image, based on parameters of a discriminator network, and reducing a generator cost function training a discriminator includes: inputting an output image of the trained generator network and the high-resolution color image to the discriminator network, respectively; training parameters of the discriminator network by reducing a discriminator cost function (S204) the generator cost function and the discriminator cost function represent a degree in which the output image of the generator network corresponds to the high-resolution color image.

Abstract: An image processing system, an image processing method and a display device are provided. The image processing system includes at least one resolution conversion sub-system. The resolution conversion sub-system includes a CNN module, a combiner and an activation module connected in a cascaded manner. The CNN module is configured to perform convolution operation on an input signal to acquire a plurality of first feature images having a first resolution. The combiner is configured to combine the first feature images into a second feature image having a second resolution greater than the first resolution. The activation module is connected to the combiner and configured to perform a selection operation on the second feature image using an activation function.

Abstract: The present disclosure relates to an image processing method. The image processing method may include upscaling a feature image of an input image by an upscaling convolutional network to obtain a upscaled feature image; downscaling the upscaled feature image by a downscaling convolutional network to obtain a downscaled feature image; determining a residual image between the downscaled feature image and the feature image of the input image; upscaling the residual image between the downscaled feature image and the feature image of the input image to obtain an upscaled residual image; correcting the upscaled feature image using the upscaled residual image to obtain a corrected upscaled feature image; and generating a first super-resolution image based on the input image using the corrected upscaled feature image.

Abstract: The present disclosure discloses a convolutional neural network processor, an image processing method and an electronic device. The method includes: receiving, by the first convolutional unit, the input image to be processed, extracting the N feature maps with different scales in the image to be processed, sending the N feature maps to the second convolutional unit, and sending the first feature map to the processing unit; fusing, by the processing unit, the received preset noise information and the first feature map, to obtain the second feature map, and sending the second feature map to the second convolutional unit; and fusing, by the second convolutional unit, the received N feature maps with the second feature map to obtain the processed image.

Abstract: A processing method and a processing device of a neural network, an evaluation method of the neural network, a data analysis method and device, and a storage medium are provided. The processing method of the neural network includes: processing an input matrix input to an N-th nonlinear layer in at least one nonlinear layer by using the N-th nonlinear layer to obtain an output matrix output by the N-th nonlinear layer; according to the input matrix and the output matrix, performing linearization processing on the N-th nonlinear layer to determine an expression of a linear function corresponding to the N-th nonlinear layer.

Abstract: The present disclosure discloses a method and device for image encoding and a method and device for image decoding. The encoding method comprises steps of: downscaling an input high resolution HR image into a low resolution LR image by a downscaler; compressing the LR image using a first compression method; extracting an index value from at least one of the HR image and the LR image; determining a parameter from a parameter database using the index value; compressing the parameter using a second compression method different from the first compression method; and obtaining a data stream by merging the compressed parameter and the compressed LR image.

Abstract: There are disclosed an image compression method, device, and compression/decompression system, the image compression method including: receiving an input image; encoding the input image by utilizing n stages of encoding unit connected in cascades to produce an output image, where n is an integer greater than 1, wherein an input of an i-th stage of encoding unit is an i-th stage of encoding input image and includes mi?1 image components, an output of the i-th stage of encoding unit is an i-th stage of encoding output image and includes mi image components, and the output of the i-th stage of encoding unit is an input of a (i+1)-th stage of encoding unit, where 1?i<n, m is an integer greater than 1; wherein the output image includes one image component as a reference image of the input image and mn?1 image components corresponding to the input image.

Abstract: The present disclosure provides a training method for generative adversarial network, which includes: extracting a first-resolution sample image from a second-resolution sample image; separately providing a first input image and a second input image for a generative network to generate a first output image and a second output image respectively, the first input image including a first-resolution sample image and a first noise image, the second input image including the first-resolution sample image and a second noise image; separately providing the first output image and a second-resolution sample image for a discriminative network to output a first discrimination result and a second discrimination result; and adjusting parameters of the generative network to reduce a loss function. The present disclosure further provides an image processing method using the generative adversarial network, a computer device, and a computer-readable storage medium.

Abstract: An image processing apparatus and method, an image processing system and a training method are disclosed. The image processing method comprises: receiving an input image; and inputting the input image to K stages of cascaded decoding units, to process the input image to obtain an output image, wherein an ith stage of decoding unit receives mK+1?i input images and outputs mK?i output images, a resolution of the output images is greater than a resolution of the input images, and the number of image components of the output images is less than the number of image components of the input images, where K, i and m are positive integers and 1?i?K.

Abstract: Disclosed is a two-dimensional code image generation method and apparatus, a storage medium and an electronic device related to the field of two-dimensional code image technology. The method includes obtaining an initial two-dimensional code image and a background image, and performing structured processing on the initial two-dimensional code image according to the background image to obtain a structured two-dimensional code image, performing mode transfer processing on the background image to obtain a background image of a target mode by a mode transfer model, and performing a fusion operation on the structured two-dimensional code image and the background image of the target mode to obtain a target two-dimensional code image.

Abstract: The present disclosure provides a method and an apparatus for stretching an image. The method for stretching an image includes: selecting a corresponding stretching mode according to a mode selection parameter; generating a corresponding stretching filter group according to a stretching parameter and the selected stretching mode, and segmenting input image data into blocks; and processing the input image data segmented into blocks by the stretching filter group, to obtain stretched image data.

Abstract: The embodiments of the present disclosure provide an image processing method, and a processing device. The image processing method comprises: acquiring a first image including N components, where N is a positive integer greater than or equal to 1; and performing image conversion processing on the first image using a generative neural network, to output a first output image, wherein the generative neural network is trained using a Laplace transform function.

Abstract: Provided are an image encoding method, an image decoding method and an image processing system including image encoding/decoding apparatus. The image encoding method includes steps of: acquiring a first image and a plurality of second images; updating features of each second image of the plurality of second images to obtain corresponding update features; superposing the first image with the update features of each second image of the plurality of second images to generate superposed images; generating a plurality of prediction images according to the superposed images; determining difference features between each second image of the plurality of second images and a corresponding prediction image; outputting the superposed images and the difference features; wherein the updating and/or predicting adopts a convolutional neural network.

Abstract: An image processing method implemented by a computing device is described herein, which includes acquiring an image to be processed and a target style of image, the image to be processed being an image of a second resolution level, and inputting the image to be processed and the target style into a trained image processing neural network for image processing to obtain a target image of the target style, the target image being an image of a first resolution level. The resolution of the image of the first resolution level is higher than that of the image of the second resolution level.