RSTC:用于图像去噪的近似泰勒扩展的残差斯温变换级联方法

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jin Liu , Yang Yang , Biyun Xu , Hao Yu , Yaozong Zhang , Qian Li , Zhenghua Huang
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引用次数: 0

摘要

传统的去噪方法通过采用不同的前验建立数学模型,虽然可以取得较好的效果,但通常耗时较长,而且其输出对正则化参数不具有自适应性。而端到端深度学习去噪策略的成功取决于大量数据,缺乏理论上的可解释性。针对上述问题,本文提出了一种基于泰勒展开的新型图像去噪方法,即残差斯文变换级联(Residual Swin Transformer Cascade,RSTC)。RSTC 的主要流程如下:首先,我们讨论了图像去噪模型与泰勒展开及其相邻导数部分之间的关系。其次,我们使用轻量级可变形卷积神经网络来估计泰勒展开的基本层,并使用残差网络,其中选择斯温变换器块作为主干来寻求导数层的解决方案。最后,这两个网络的结果有助于泰勒展开的近似解。在实验中,我们首先测试并讨论了网络参数的选择,以验证其有效性。结果表明,我们的方法具有强大的泛化能力,在性能改善和结构保持方面优于最先进的去噪方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RSTC: Residual Swin Transformer Cascade to approximate Taylor expansion for image denoising

Traditional denoising methods establish mathematical models by employing different priors, which can achieve preferable results but they are usually time-consuming and their outputs are not adaptive on regularization parameters. While the success of end-to-end deep learning denoising strategies depends on a large amount of data and lacks a theoretical interpretability. In order to address the above problems, this paper proposes a novel image denoising method, namely Residual Swin Transformer Cascade (RSTC), based on Taylor expansion. The key procedures of our RSTC are specified as follows: Firstly, we discuss the relationship between image denoising model and Taylor expansion, as well as its adjacent derivative parts. Secondly, we use a lightweight deformable convolutional neural network to estimate the basic layer of Taylor expansion and a residual network where swin transformer block is selected as a backbone for pursuing the solution of the derivative layer. Finally, the results of the two networks contribute to the approximation solution of Taylor expansion. In the experiments, we firstly test and discuss the selection of network parameters to verify its effectiveness. Then, we compare it with existing advanced methods in terms of visualization and quantification, and the results show that our method has a powerful generalization ability and performs better than state-of-the-art denoising methods on performance improvement and structure preservation.

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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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