Designing and Training of A Dual CNN for Image Denoising
- URL: http://arxiv.org/abs/2007.03951v1
- Date: Wed, 8 Jul 2020 08:16:24 GMT
- Title: Designing and Training of A Dual CNN for Image Denoising
- Authors: Chunwei Tian, Yong Xu, Wangmeng Zuo, Bo Du, Chia-Wen Lin and David
Zhang
- Abstract summary: We propose a Dual denoising Network (DudeNet) to recover a clean image.
DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block.
- Score: 117.54244339673316
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep convolutional neural networks (CNNs) for image denoising have recently
attracted increasing research interest. However, plain networks cannot recover
fine details for a complex task, such as real noisy images. In this paper, we
propsoed a Dual denoising Network (DudeNet) to recover a clean image.
Specifically, DudeNet consists of four modules: a feature extraction block, an
enhancement block, a compression block, and a reconstruction block. The feature
extraction block with a sparse machanism extracts global and local features via
two sub-networks. The enhancement block gathers and fuses the global and local
features to provide complementary information for the latter network. The
compression block refines the extracted information and compresses the network.
Finally, the reconstruction block is utilized to reconstruct a denoised image.
The DudeNet has the following advantages: (1) The dual networks with a parse
mechanism can extract complementary features to enhance the generalized ability
of denoiser. (2) Fusing global and local features can extract salient features
to recover fine details for complex noisy images. (3) A Small-size filter is
used to reduce the complexity of denoiser. Extensive experiments demonstrate
the superiority of DudeNet over existing current state-of-the-art denoising
methods.
Related papers
- Two-stage Progressive Residual Dense Attention Network for Image
Denoising [0.680228754562676]
Many deep CNN-based denoising models equally utilize the hierarchical features of noisy images without paying attention to the more important and useful features, leading to relatively low performance.
We design a new Two-stage Progressive Residual Attention Network (TSP-RDANet) for image denoising, which divides the whole process of denoising into two sub-tasks to remove noise progressively.
Two different attention mechanism-based denoising networks are designed for the two sequential sub-tasks.
arXiv Detail & Related papers (2024-01-05T14:31:20Z) - 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) - Dual Residual Attention Network for Image Denoising [19.978731146465822]
In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise.
We propose a novel Dual-branch Residual Attention Network (DRANet) for image denoising.
Our DRANet can produce competitive denoising performance both on synthetic and real-world noise removal.
arXiv Detail & Related papers (2023-05-07T13:11:55Z) - 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) - Thunder: Thumbnail based Fast Lightweight Image Denoising Network [92.9631117239565]
A textbfThumbtextbfnail based textbfDtextbfenoising Netwotextbfrk dubbed Thunder is proposed.
arXiv Detail & Related papers (2022-05-24T06:38:46Z) - Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network.
We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
arXiv Detail & Related papers (2022-04-19T17:59:45Z) - 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) - Image Denoising for Strong Gaussian Noises With Specialized CNNs for
Different Frequency Components [4.010371060637209]
In machine learning approach to image denoising a network is trained to recover a clean image from a noisy one.
In this paper a novel structure is proposed based on training multiple specialized networks.
arXiv Detail & Related papers (2020-11-26T23:20:25Z) - Identity Enhanced Residual Image Denoising [61.75610647978973]
We learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising.
The proposed network produces remarkably higher numerical accuracy and better visual image quality than the classical state-of-the-art and CNN algorithms.
arXiv Detail & Related papers (2020-04-26T04:52:22Z)
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.