Gated Texture CNN for Efficient and Configurable Image Denoising

• URL: http://arxiv.org/abs/2003.07042v2
• Date: Mon, 20 Apr 2020 01:59:52 GMT
• Title: Gated Texture CNN for Efficient and Configurable Image Denoising
• Authors: Kaito Imai and Takamichi Miyata
• Abstract summary: Convolutional neural network (CNN)-based image denoising methods estimate the noise component contained in a noisy input image and restore a clean image by subtracting the estimated noise from the input. Previous denoising methods tend to remove high-frequency information (e.g., textures) from the input. We propose a gated texture CNN (GTCNN), which is designed to carefully exclude the texture information from each intermediate feature map of the CNN by incorporating gating mechanisms. Our GTCNN state-of-the-art performance with 4.8 times fewer parameters than previous state-of-the-art methods.
• Score: 6.09170287691728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural network (CNN)-based image denoising methods typically estimate the noise component contained in a noisy input image and restore a clean image by subtracting the estimated noise from the input. However, previous denoising methods tend to remove high-frequency information (e.g., textures) from the input. It caused by intermediate feature maps of CNN contains texture information. A straightforward approach to this problem is stacking numerous layers, which leads to a high computational cost. To achieve high performance and computational efficiency, we propose a gated texture CNN (GTCNN), which is designed to carefully exclude the texture information from each intermediate feature map of the CNN by incorporating gating mechanisms. Our GTCNN achieves state-of-the-art performance with 4.8 times fewer parameters than previous state-of-the-art methods. Furthermore, the GTCNN allows us to interactively control the texture strength in the output image without any additional modules, training, or computational costs.

Related papers

• Fast, nonlocal and neural: a lightweight high quality solution to image denoising [19.306450225657414]
  convolutional neural networks (CNNs) are now outperformed by model based denoising algorithms. We propose a solution by combining a nonlocal algorithm with a lightweight residual CNN. Our solution is between 10 and 20 times faster than CNNs with equivalent performance and attains higher PSNR.
  arXiv  Detail & Related papers  (2024-03-06T06:12:56Z)
• A cross Transformer for image denoising [83.68175077524111]
  We propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising.
  arXiv  Detail & Related papers  (2023-10-16T13:53:19Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Decoupled Mixup for Generalized Visual Recognition [71.13734761715472]
  We propose a novel "Decoupled-Mixup" method to train CNN models for visual recognition. Our method decouples each image into discriminative and noise-prone regions, and then heterogeneously combines these regions to train CNN models. Experiment results show the high generalization performance of our method on testing data that are composed of unseen contexts.
  arXiv  Detail & Related papers  (2022-10-26T15:21:39Z)
• 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)
• Deep neural networks-based denoising models for CT imaging and their efficacy [0.3058685580689604]
  We aim to examine the image quality of the Deep Neural Networks (DNNs) results from a holistic viewpoint for low-dose CT image denoising. We build a library of advanced DNN denoising architectures such as the DnCNN, U-Net, Red-Net, GAN, etc. Each network is modeled, as well as trained, such that it yields its best performance in terms of the PSNR and SSIM.
  arXiv  Detail & Related papers  (2021-11-18T06:18:26Z)
• 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)
• 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)
• Unrolling of Deep Graph Total Variation for Image Denoising [106.93258903150702]
  In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design. We employ interpretable analytical low-pass graph filters and employ 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN.
  arXiv  Detail & Related papers  (2020-10-21T20:04:22Z)
• CycleISP: Real Image Restoration via Improved Data Synthesis [166.17296369600774]
  We present a framework that models camera imaging pipeline in forward and reverse directions. By training a new image denoising network on realistic synthetic data, we achieve the state-of-the-art performance on real camera benchmark datasets.
  arXiv  Detail & Related papers  (2020-03-17T15:20:25Z)

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.