1 code implementation • 8 Jun 2022 • Pengju Liu, Hongzhi Zhang, Jinghui Wang, Yuzhi Wang, Dongwei Ren, WangMeng Zuo
In particular, we take well-trained CBDNet, NBNet, HINet, Uformer and GMSNet into denoiser pool, and a U-Net is adopted to predict pixel-wise weighting maps to fuse these denoisers.
3 code implementations • 6 Jul 2019 • Pengju Liu, Hongzhi Zhang, Wei Lian, WangMeng Zuo
Specifically, MWCNN for image restoration is based on U-Net architecture, and inverse wavelet transform (IWT) is deployed to reconstruct the high resolution (HR) feature maps.
5 code implementations • 18 May 2018 • Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, WangMeng Zuo
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
Ranked #2 on Grayscale Image Denoising on Set12 sigma25