Abstract: A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

Abstract: A frame for vehicle and a vehicle, the frame including a frame assembly and a telescopic assembly, the frame assembly including a front frame and a rear frame; the telescopic assembly including a driving telescopic member for connecting to a driver, one end of the driving telescopic member connected to the front frame and the other end connected to the rear frame for extending or shrinking along a first direction under the drive of the driver so as to drive the front frame and the rear frame to extend or shrink along the first direction. The extending or shrinking of the frame is enabled by the above manner.

Abstract: The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

Abstract: A method includes: computing noise data by subtracting, by a processing circuit, a noisy image from a corresponding ground truth image; clustering, by the processing circuit, a plurality of noise values of the noise data based on intensity values of the corresponding ground truth image; permuting, by the processing circuit, a plurality of locations of the noise values of the noise data within each cluster; generating, by the processing circuit, a synthetic noise image based on the permuted locations of the noise values; adding, by the processing circuit, the synthetic noise image to the corresponding ground truth image to generate a synthetic noisy image; and augmenting an image dataset for training a neural network to perform image denoising with the synthetic noisy image.

Abstract: A method for denoising an image includes: receiving, by a processing circuit of a user equipment, an input image; supplying, by the processing circuit, the input image to a trained convolutional neural network (CNN) including a multi-scale residual dense block (MRDB), the MRDB including: a residual dense block (RDB); and an atrous spatial pyramid pooling (ASPP) module; computing, by the processing circuit, an MRDB output feature map using the MRDB; and computing, by the processing circuit, an output image based on the MRDB output feature map, the output image being a denoised version of the input image.

Abstract: The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

Abstract: The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.

Abstract: A method for denoising an image includes: receiving, by a processing circuit of a user equipment, an input image; supplying, by the processing circuit, the input image to a trained convolutional neural network (CNN) including a multi-scale residual dense block (MRDB), the MRDB including: a residual dense block (RDB); and an atrous spatial pyramid pooling (ASPP) module; computing, by the processing circuit, an MRDB output feature map using the MRDB; and computing, by the processing circuit, an output image based on the MRDB output feature map, the output image being a denoised version of the input image.

Abstract: The present disclosure includes systems and methods for color transfer. The method includes receiving a target image, and determining dominant source colors. The method further includes transforming the target image into a color model including a target luminance component and a target color information component. Additionally, the method includes segmenting the target image into a plurality of target segments based on the target color information component or the target luminance component and extracting dominant target colors from the target image by extracting information for at least one of the dominant target colors from each target segment of the plurality of target segments. Further, the method includes generating a color mapping relationship between the dominant target colors and the dominant source colors, and creating a recolored target image using the color mapping relationship.