A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

• URL: http://arxiv.org/abs/2302.05435v1
• Date: Fri, 10 Feb 2023 18:51:19 GMT
• Title: A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks
• Authors: Ahmad Ali Rafiee, Mahmoud Farhang
• Abstract summary: We propose a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been an unprecedented upsurge in applying deep learning approaches, specifically convolutional neural networks (CNNs), to solve image denoising problems, owing to their superior performance. However, CNNs mostly rely on Gaussian noise, and there is a conspicuous lack of exploiting CNNs for salt-and-pepper (SAP) noise reduction. In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images. To meet this objective, we introduce a new selective convolutional (SeConv) block. SeConvNet is compared to state-of-the-art SAP denoising methods using extensive experiments on various common datasets. The results illustrate that the proposed SeConvNet model effectively restores images corrupted by SAP noise and surpasses all its counterparts at both quantitative criteria and visual effects, especially at high and very high noise densities.

Related papers

• Multi-stage image denoising with the wavelet transform [125.2251438120701]
  Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. We propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB)
  arXiv  Detail & Related papers  (2022-09-26T03:28:23Z)
• Median Pixel Difference Convolutional Network for Robust Face Recognition [8.636370632339785]
  Existing face recognition algorithms based on convolutional neural networks (CNNs) are vulnerable to noise. Noise corrupted image patterns could lead to false activations, significantly decreasing face recognition accuracy in noisy situations. We propose a Median Pixel Difference Convolutional Network (MeDiNet) to equip CNNs with built-in robustness to noise of different levels.
  arXiv  Detail & Related papers  (2022-05-30T13:15:49Z)
• 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)
• 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)
• 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)
• Image Denoising using Attention-Residual Convolutional Neural Networks [0.0]
  We propose a new learning-based non-blind denoising technique named Attention Residual Convolutional Neural Network (ARCNN) and its extension to blind denoising named Flexible Attention Residual Convolutional Neural Network (FARCNN) ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for Gaussian and Poisson denoising, respectively FARCNN presented very consistent results, even with slightly worsen performance compared to ARCNN.
  arXiv  Detail & Related papers  (2021-01-19T16:37:57Z)
• Deep Universal Blind Image Denoising [26.77629755630694]
  Deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets. We present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective.
  arXiv  Detail & Related papers  (2021-01-18T11:49:21Z)
• 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)
• Enhancement of a CNN-Based Denoiser Based on Spatial and Spectral Analysis [23.11994688706024]
  We propose a discrete wavelet denoising CNN (WDnCNN) which restores images corrupted by various noise with a single model. To address this issue, we present a band normalization module (BNM) to normalize the coefficients from different parts of the frequency spectrum. We evaluate the proposed WDnCNN, and compare it with other state-of-the-art denoisers.
  arXiv  Detail & Related papers  (2020-06-28T05:25:50Z)
• Deep Learning on Image Denoising: An overview [92.07378559622889]
  We offer a comparative study of deep techniques in image denoising. We first classify the deep convolutional neural networks (CNNs) for additive white noisy images. Next, we compare the state-of-the-art methods on public denoising datasets in terms of quantitative and qualitative analysis.
  arXiv  Detail & Related papers  (2019-12-31T05:03:57Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.