Denoising Textures is a Different Ballgame Altogether!

Key points

• The human brain effortlessly deciphers colors and textures from visual stimuli and reconstructs the world around us, yet preserving such details remains challenging in image denoising.1
• While significant progress has been made in generic image denoising, the problem of texture denoising has remained relatively under-explored.1
• Surprisingly, fine-tuning on texture datasets result in the models catastrophically forgetting the original dataset.1
• Since denoising quality directly impacts downstream tasks such as generative modelling, we argue that dedicated texture aware denoisers are essential for faithful texture reconstruction and synthesis.1

Sources (1)

• [1]Denoising Textures is a Different Ballgame Altogether!
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:27 PM

  In this paper, we analyze the behavior of state-of-the-art (SOTA) denoisers on texture datasets and show that performance improvements are possible only by fine-tuning the denoisers on the texture datasets.

  The human brain effortlessly deciphers colors and textures from visual stimuli and reconstructs the world around us, yet preserving such details remains challenging in image denoising.

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