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Generic 3D Convolutional Fusion for Image Restoration

Jiqing Wu and Radu Timofte and Luc Van Gool
Asian Conference on Computer Vision Workshops (ACCVW 2016)
November 2016

Abstract

Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning techniques contributed to this significantly. The top methods differ in their formulations and assumptions, so even if their average performance may be similar, some work better on certain image types and image regions than others. This complementarity motivated us to propose a novel 3D convolutional fusion (3DCF) method. Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denoising and single image super-resolution. Our 3DCF method achieves substantial improvements (0.1dB-0.4dB PSNR) over the state-of-the-art methods that it fuses on standard benchmarks for both tasks. At the same time, the method still is computationally efficient.


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@InProceedings{eth_biwi_01313,
  author = {Jiqing Wu and Radu Timofte and Luc Van Gool},
  title = {Generic 3D Convolutional Fusion for Image Restoration},
  booktitle = {Asian Conference on Computer Vision Workshops (ACCVW 2016)},
  year = {2016},
  month = {November},
  publisher = {Springer},
  keywords = {}
}