Noise2kernel: adaptive self-supervised blind denoising using a dilated convolutional kernel architecture

HIGHLIGHTS

• who: Kanggeun Lee and Won-Ki Jeong from the Department of Computer Science and Engineering, Korea University, Seoul, Korea have published the research work: Noise2Kernel: Adaptive Self-Supervised Blind Denoising Using a Dilated Convolutional Kernel Architecture, in the Journal: Sensors 2022, 22, 4255. of /2022/
• what: The authors propose network that satisfies an invariant property allowing efficient kernel-based training without random masking. The authors demonstrate the efficacy of the proposed method by comparing it with state-of-the-art methods various examples. To the novel network architecture, the authors propose a novel adaptive self . . .
  

   

  

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