Intriguing Findings of Frequency Selection for Image Deblurring

Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image. Recent progress on image deblurring always designs end-to-end architectures and aims at learning the difference between blurry and sharp image pairs from pixel-level, which inevitably overlooks the importance of blur kernels. This paper reveals an intriguing phenomenon that simply applying ReLU operation on the frequency domain of a blur image followed by inverse Fourier transform, i.e., frequency selection, provides faithful information about the blur pattern (e.g., the blur direction and blur level, implicitly shows the kernel pattern). Based on this observation, we attempt to…

• NS
  Natural Science Foundation of Shanghai

  Award: 21ZR1420800
• NN
  National Natural Science Foundation of China

  Awards: 62101191, 61975056, 22DZ2229004
• SA
  Science and Technology Commission of Shanghai Municipality

  Awards: 20440713100, 22DZ2229004