Abstract: In an embodiment, a method includes receiving and processing a first color image by an encoder. The first color image includes a first portion of the first color image and a second portion of the first color image located at different locations of the first color image. The encoder is configured to output at least one first feature map including fused global information and local information such that whether a color consistency relationship between the first portion of the first color image and the second portion of the first color image exists is encoded into the fused global information and local information.

Abstract: A method includes receiving and processing a first image by an encoder-decoder network. The first image includes a first portion and a second portion located at different locations. The encoder-decoder network includes an encoder and a decoder. The encoder is configured to output at least one feature map including global information and local information such that whether a color consistency relationship between the first portion and the second portion of the first image exists is encoded into the global information and the local information. The decoder is configured to output a second image generated from the at least one feature map, wherein a first portion of the second image corresponding to the first portion of the first image and a second portion of the second image corresponding to the second portion of the first image are restored considering whether the color consistency relationship exists.

Abstract: In an embodiment, a method includes receiving a low-light digital image; generating, by at least one processor, a resulting digital image by processing the low-light digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upsampling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the low-light digital image and employed between the downsampling stage and the upsampling stage; and outputting, by the at least one processor, the resulting digital image to an output device. In the upsampling stage, spatial resolution increases by using a bilinear interpolation operation performed before every few convolutional layers to speed up the inference time of the network.

Abstract: A solar cell and preparation method. The solar cell includes silicon substrate having first or second polarity, where the substrate includes first and second sides opposite to each other; first passivation structure on first side of the substrate, a portion of first structure farthest from the substrate having first polarity and a position where first structure is located being first electrode region; second passivation structure on a side of first structure away from the substrate, a portion of second structure farthest from the substrate having second polarity and a position where second structure is located being second electrode region, second and first electrode regions are not overlapped and second structure has a process temperature lower than first structure; and first electrode in first region on a side of second structure away from the substrate and second electrode in second region on a side of second structure away from the substrate.

Abstract: In an embodiment, a method includes receiving a digital image; generating, by at least one processor, a resulting digital image by processing the digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upsampling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the digital image and employed between the downsampling stage and the upsampling stage; and outputting, by the at least one processor, the resulting digital image to an output device.

Abstract: A method for training a model, the method including: defining a primary model for identifying a class of input data based on a first characteristic of the input data; defining a secondary model for detecting a change to a second characteristic between multiple input data captured at different times; defining a forward link from an output of an intermediate layer of the secondary model to an input of an intermediate layer of the primary model; and training the primary model and the secondary model in parallel based on a training set of input data.

Abstract: A method includes: aggregating information from a corresponding combination of all of multi-scale first dimensional receptive fields and each of multi-scale second dimensional receptive fields, so that information from multi-scale first and second dimensional receptive fields corresponding to the multi-scale second dimensional receptive fields is obtained; wherein the multi-scale first dimensional receptive fields being first dimensional is being one of spatial and temporal, and the multi-scale second dimensional receptive fields being second dimensional is being the other of spatial and temporal; wherein a corresponding first convolutional neural network operation set provides each of the multi-scale second dimensional receptive fields and each operation of the first convolutional neural network operation set has a corresponding first dimensional local to local constraint; and aggregating the information from the multi-scale first and second dimensional receptive fields to obtain aggregated multi-scale first

Abstract: An image processing method, an electronic device, and a computer-readable storage medium are provided. In the method, a first image is processed through a U-net to obtain a second image, and the second image is a noise map of the first image. The U-net includes an encoding network, a decoding network, and a bottleneck network between the encoding network and the decoding network. The bottleneck network includes a global pooling layer, a bilinear upscaling layer, and a 1×1 convolutional layer. Moreover, a third image is generated according to the first image and the second image, and the third image is a denoised map of the first image.

Abstract: In an embodiment, a method includes receiving a low-light digital image; generating, by at least one processor, a resulting digital image by processing the low-light digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upscaling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the low-light digital image and employed between the downsampling stage and the upscaling stage; and outputting, by the at least one processor, the resulting digital image to an output device.

Abstract: A tensor processing method and apparatus, and an electronic device are provided. In the method, a first matrix is determined based on a first tensor, and a first sub-matrix is extracted from the first matrix. The first matrix includes all elements of the first tensor, and the first sub-matrix includes all elements of the first subtensor, and the first subtensor is a subset of the first tensor.

Abstract: Provided are method and apparatus for image processing. A neural network includes an encoding network, an intermediate network, and a decoding network including multiple input layers and an output layer. In the method, at an input layer of the decoding network, first output data is received from a previous layer, and a first operation is performed on the first output data to obtain first input data of the input layer, the input layer is any one of multiple input layers. At the input layer, second output data is received from a corresponding layer of the encoding network, and a second operation is performed on the second output data to obtain second input data of the input layer. Output data of the input layer is obtained according to the first and second input data. Operations are performed in a next layer based on the output data to obtain an output image.

Abstract: A convolution method, an electronic device and a non-transitory computer-readable storage medium are provided. The method includes that: multiple resultant matrices respectively corresponding to multiple 1×1 convolution kernel elements in a filter are added to different sub-regions of a first output matrix, to obtain an accumulating feature of the first output matrix, and a second output matrix is extracted from the first output matrix with the accumulating feature. A size of the second output matrix is less than a size of the first output matrix.

Abstract: Provided are a method and apparatus for image processing and a terminal. In the method, an input image is received and processed in a neural network to obtain an output image according to global information of the input image. The terminal includes at least one processor; and a memory coupled with the at least one processor and configured to store instructions which, when executed by the at least one processor, are operable with the at least one processor to implement a neural network to receive an input image and process the input image in the neural network to obtain an output image according to global information of the input image.

Abstract: The present disclosure relates to a Temporal Information Aggregation (TIA) neural network block to extract underlying multiscale temporal information. By applying TIA, information in different temporal scales may be effectively extracted. The TIA block may be implemented as a block and thus may be inserted into any architectures. The extracted multi-scale temporal information contributes to the final output as a residual.

Abstract: A method includes: receiving a facial image (204); obtaining a facial shape (206) using the facial image (204); defining, using the facial image (204) and the facial shape (206), a plurality of facial component-specific local regions, wherein each of the facial component-specific local regions includes a corresponding separately considered facial component of a plurality of separately considered facial components from the facial image (204), and the corresponding separately considered facial component of the separately considered facial components corresponds to a corresponding first facial landmark set (208) of a plurality of first facial landmark sets in the facial shape (206); for each of the facial component-specific local regions, performing a cascaded regression method using each of the facial component-specific local regions and a corresponding facial landmark set (208) of the first facial landmark sets to obtain a corresponding facial landmark set (210) of a plurality of second facial landmark sets.

Abstract: In an embodiment, a method includes receiving and processing a first color image by an encoder. The first color image includes a first portion of the first color image and a second portion of the first color image located at different locations of the first color image. The encoder is configured to output at least one first feature map including fused global information and local information such that whether a color consistency relationship between the first portion of the first color image and the second portion of the first color image exists is encoded into the fused global information and local information.

Abstract: In an embodiment, a computer-implemented method includes receiving and processing a first image, and outputting a first feature map by an encoder. The encoder includes a plurality of first convolutional stages that receive the first image and output stage-by-stage a plurality of second feature maps corresponding to the first convolutional stages. The second feature maps have gradually decreased scales. For each second convolutional stage of the first convolutional stages, a first skip connection is added between each second convolutional stage and each of at least one remaining convolutional stage of the first convolutional stages corresponding to each second convolutional stage.

Abstract: In an embodiment, a method includes receiving a digital image; generating, by at least one processor, a resulting digital image by processing the digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upsampling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the digital image and employed between the downsampling stage and the upsampling stage; and outputting, by the at least one processor, the resulting digital image to an output device.

Abstract: A method includes receiving and processing a first image by an encoder-decoder network. The first image includes a first portion and a second portion located at different locations. The encoder-decoder network includes an encoder and a decoder. The encoder is configured to output at least one feature map including global information and local information such that whether a color consistency relationship between the first portion and the second portion of the first image exists is encoded into the global information and the local information. The decoder is configured to output a second image generated from the at least one feature map, wherein a first portion of the second image corresponding to the first portion of the first image and a second portion of the second image corresponding to the second portion of the first image are restored considering whether the color consistency relationship exists.

Abstract: In an embodiment, a method includes receiving a low-light digital image; generating, by at least one processor, a resulting digital image by processing the low-light digital image with an encoder-decoder neural network comprising a plurality of convolutional layers classified into a downsampling stage and an upsampling stage, and a multi-scale context aggregating block configured to aggregate multi-scale context information of the low-light digital image and employed between the downsampling stage and the upsampling stage; and outputting, by the at least one processor, the resulting digital image to an output device. In the upsampling stage, spatial resolution increases by using a bilinear interpolation operation performed before every few convolutional layers to speed up the inference time of the network.