Efficient Blind-Spot Neural Network Architecture for Image Denoising

• URL: http://arxiv.org/abs/2008.11010v1
• Date: Tue, 25 Aug 2020 13:48:40 GMT
• Title: Efficient Blind-Spot Neural Network Architecture for Image Denoising
• Authors: David Honz\'atko, Siavash A. Bigdeli, Engin T\"uretken, L. Andrea Dunbar
• Abstract summary: We propose a novel fully convolutional network architecture that uses dilations to achieve the blind-spot property. Our network achieves the performance over the prior work and state-of-the-art results on established datasets.
• Score: 4.513547390985147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image denoising is an essential tool in computational photography. Standard denoising techniques, which use deep neural networks at their core, require pairs of clean and noisy images for its training. If we do not possess the clean samples, we can use blind-spot neural network architectures, which estimate the pixel value based on the neighbouring pixels only. These networks thus allow training on noisy images directly, as they by-design avoid trivial solutions. Nowadays, the blind-spot is mostly achieved using shifted convolutions or serialization. We propose a novel fully convolutional network architecture that uses dilations to achieve the blind-spot property. Our network improves the performance over the prior work and achieves state-of-the-art results on established datasets.

Related papers

• Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network [12.478287906337194]
  Most representative self-supervised denoisers are based on blind-spot networks. A standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise. We propose a novel self-supervised training framework that can remove real noise.
  arXiv  Detail & Related papers  (2023-04-19T08:55:27Z)
• Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising [73.71324390085714]
  We propose a novel perspective to solve the problem of real-world sRGB image denoising. We take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. We present a locally aware network (LAN) to meet the requirement, while LAN itself is supervised with the output of BNN.
  arXiv  Detail & Related papers  (2023-03-27T06:18:20Z)
• NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images [79.33220095067749]
  Large amounts of blurry-clean image pairs are required for training to achieve good performance. Deep networks often fail to perform well when the blurry images and the blur kernels during testing are very different from the ones used during training.
  arXiv  Detail & Related papers  (2022-09-20T06:21:11Z)
• Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis [148.16279746287452]
  We propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block. For the training data synthesis, we design a practical noise degradation model which takes into consideration different kinds of noise. Experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-24T18:11:31Z)
• Selective Residual M-Net for Real Image Denoising [6.909688694501238]
  We propose a blind real image denoising network (SRMNet) to advance the performance of denoising algorithms. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. OurNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2022-03-03T11:10:30Z)
• Self-Denoising Neural Networks for Few Shot Learning [66.38505903102373]
  We present a new training scheme that adds noise at multiple stages of an existing neural architecture while simultaneously learning to be robust to this added noise. This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures.
  arXiv  Detail & Related papers  (2021-10-26T03:28:36Z)
• Efficient Deep Image Denoising via Class Specific Convolution [24.103826414190216]
  We propose an efficient deep neural network for image denoising based on pixel-wise classification. The proposed method can reduce the computational costs without sacrificing the performance.
  arXiv  Detail & Related papers  (2021-03-02T10:28:15Z)
• Exploring ensembles and uncertainty minimization in denoising networks [0.522145960878624]
  We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.
  arXiv  Detail & Related papers  (2021-01-24T20:48:18Z)
• Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [98.82804259905478]
  We present Neighbor2Neighbor to train an effective image denoising model with only noisy images. In detail, input and target used to train a network are images sub-sampled from the same noisy image. A denoising network is trained on sub-sampled training pairs generated in the first stage, with a proposed regularizer as additional loss for better performance.
  arXiv  Detail & Related papers  (2021-01-08T02:03:25Z)
• Reconstructing the Noise Manifold for Image Denoising [56.562855317536396]
  We introduce the idea of a cGAN which explicitly leverages structure in the image noise space. By learning directly a low dimensional manifold of the image noise, the generator promotes the removal from the noisy image only that information which spans this manifold. Based on our experiments, our model substantially outperforms existing state-of-the-art architectures.
  arXiv  Detail & Related papers  (2020-02-11T00:31:31Z)
• Spatial-Adaptive Network for Single Image Denoising [14.643663950015334]
  We propose a novel spatial-adaptive denoising network (SADNet) for efficient single image blind noise removal. Our method can surpass the state-of-the-art denoising methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2020-01-28T12:24:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.