{
  "id": "2303.16783",
  "version": "v1",
  "published": "2023-03-29T15:19:01.000Z",
  "updated": "2023-03-29T15:19:01.000Z",
  "title": "Exploring Asymmetric Tunable Blind-Spots for Self-supervised Denoising in Real-World Scenarios",
  "authors": [
    "Shiyan Chen",
    "Jiyuan Zhang",
    "Zhaofei Yu",
    "Tiejun Huang"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However, noise in real-world scenarios is often spatially correlated, which causes many self-supervised algorithms based on the pixel-wise independent noise assumption to perform poorly on real-world images. Recently, asymmetric pixel-shuffle downsampling (AP) has been proposed to disrupt the spatial correlation of noise. However, downsampling introduces aliasing effects, and the post-processing to eliminate these effects can destroy the spatial structure and high-frequency details of the image, in addition to being time-consuming. In this paper, we systematically analyze downsampling-based methods and propose an Asymmetric Tunable Blind-Spot Network (AT-BSN) to address these issues. We design a blind-spot network with a freely tunable blind-spot size, using a large blind-spot during training to suppress local spatially correlated noise while minimizing damage to the global structure, and a small blind-spot during inference to minimize information loss. Moreover, we propose blind-spot self-ensemble and distillation of non-blind-spot network to further improve performance and reduce computational complexity. Experimental results demonstrate that our method achieves state-of-the-art results while comprehensively outperforming other self-supervised methods in terms of image texture maintaining, parameter count, computation cost, and inference time.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-03-29T15:19:01.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "exploring asymmetric tunable blind-spots",
      "real-world scenarios",
      "self-supervised denoising",
      "blind-spot network",
      "method achieves state-of-the-art results"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}