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公开(公告)号:US20230043310A1
公开(公告)日:2023-02-09
申请号:US17972961
申请日:2022-10-25
Applicant: Samsung Electronics Co., Ltd.
Inventor: Zengli Yang , Long Bao , Shuangquan Wang , Dongwoon Bai , Jungwon Lee
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.
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公开(公告)号:US20210287342A1
公开(公告)日:2021-09-16
申请号:US17010670
申请日:2020-09-02
Applicant: Samsung Electronics Co., Ltd.
Inventor: Zengli Yang , Long Bao , Shuangquan Wang , Dongwoon Bai , Jungwon Lee
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.
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公开(公告)号:US11508037B2
公开(公告)日:2022-11-22
申请号:US17010670
申请日:2020-09-02
Applicant: Samsung Electronics Co., Ltd.
Inventor: Zengli Yang , Long Bao , Shuangquan Wang , Dongwoon Bai , Jungwon Lee
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.
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