Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising

• URL: http://arxiv.org/abs/2112.11779v1
• Date: Wed, 22 Dec 2021 10:35:53 GMT
• Title: Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising
• Authors: Zhuang Jia
• Abstract summary: We propose a novel network architecture denoted as IGNet, in order to refine the frequency bands from low to high in a progressive manner. With this design, more inter-frequency prior and information are utilized, thus the model size can be lightened while still perserves competitive results.
• Score: 1.52292571922932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image denoising is of vital importance in many imaging or computer vision related areas. With the convolutional neural networks showing strong capability in computer vision tasks, the performance of image denoising has also been brought up by CNN based methods. Though CNN based image denoisers show promising results on this task, most of the current CNN based methods try to learn the mapping from noisy image to clean image directly, which lacks the explicit exploration of prior knowledge of images and noises. Natural images are observed to obey the reciprocal power law, implying the low-frequency band of image tend to occupy most of the energy. Thus in the condition of AGWN (additive gaussian white noise) deterioration, low-frequency band tend to preserve a higher PSNR than high-frequency band. Considering the spatial morphological consistency of different frequency bands, low-frequency band with more fidelity can be used as a guidance to refine the more contaminated high-frequency bands. Based on this thought, we proposed a novel network architecture denoted as IGNet, in order to refine the frequency bands from low to high in a progressive manner. Firstly, it decomposes the feature maps into high- and low-frequency subbands using DWT (discrete wavelet transform) iteratively, and then each low band features are used to refine the high band features. Finally, the refined feature maps are processed by a decoder to recover the clean result. With this design, more inter-frequency prior and information are utilized, thus the model size can be lightened while still perserves competitive results. Experiments on several public datasets show that our model obtains competitive performance comparing with other state-of-the-art methods yet with a lightweight structure.

Related papers

• Low-Light Enhancement in the Frequency Domain [24.195131201768096]
  Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. We propose a novel residual recurrent multi-wavelet convolutional neural network R2-MWCNN learned in the frequency domain. This end-to-end trainable network utilizes a multi-level discrete wavelet transform to divide input feature maps into distinct frequencies, resulting in a better denoise impact.
  arXiv  Detail & Related papers  (2023-06-29T08:39:34Z)
• Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising [9.119226249676501]
  Hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduce the noise in HSI data cubes, both model-driven and learning-based denoising algorithms have been proposed. This paper proposes a Degradation-Noise-Aware Unfolding Network (DNA-Net) that addresses these issues.
  arXiv  Detail & Related papers  (2023-05-06T13:28:20Z)
• Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond [14.432771193620702]
  Inspired by a latent observation that noise tends to appear in the high-frequency part of the image, we propose a fully invertible denoising method. We decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation. In this way, denoising is made tractable by inversely merging noiseless low and high-frequency parts.
  arXiv  Detail & Related papers  (2023-01-31T01:24:34Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• Frequency Disentangled Residual Network [11.388328269522006]
  Residual networks (ResNets) have been utilized for various computer vision and image processing applications. A residual block consists of few convolutional layers having trainable parameters, which leads to overfitting. A frequency disentangled residual network (FDResNet) is proposed to tackle these issues.
  arXiv  Detail & Related papers  (2021-09-26T10:52:18Z)
• Wavelet-Based Network For High Dynamic Range Imaging [64.66969585951207]
  Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. In this work, we propose a novel frequency-guided end-to-end deep neural network (FNet) to conduct HDR fusion in the frequency domain, and Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details.
  arXiv  Detail & Related papers  (2021-08-03T12:26:33Z)
• WaveFill: A Wavelet-based Generation Network for Image Inpainting [57.012173791320855]
  WaveFill is a wavelet-based inpainting network that decomposes images into multiple frequency bands. WaveFill decomposes images by using discrete wavelet transform (DWT) that preserves spatial information naturally. It applies L1 reconstruction loss to the low-frequency bands and adversarial loss to high-frequency bands, hence effectively mitigate inter-frequency conflicts.
  arXiv  Detail & Related papers  (2021-07-23T04:44:40Z)
• 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)
• Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising [80.10533234415237]
  This paper presents a multi-level wavelet residual network (MWRN) architecture as well as a progressive training scheme to improve image denoising performance. Experiments on both synthetic and real-world noisy images show that our PT-MWRN performs favorably against the state-of-the-art denoising methods.
  arXiv  Detail & Related papers  (2020-10-23T14:14:00Z)
• 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)
• Wavelet Integrated CNNs for Noise-Robust Image Classification [51.18193090255933]
  We enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT) WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.
  arXiv  Detail & Related papers  (2020-05-07T09:10:41Z)

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.