Image dejittering on the perspective of spatially-varying mixed noise removal

IF 3.4 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yingxin Zhang , Wenxing Zhang , Junping Yin
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引用次数: 0

Abstract

Jittery image is visually abnormal in jags of edge and loss of coherence. The problem of image dejittering is challenging to resolve due to the ubiquitous blur and/or noise in jittery data. In this paper, we devote to the pixel-jitter (possibly blurry) image recovery on the perspective of spatially-varying mixed noise removal. By viewing jittery image as the corruption of ideal image with outliers and spatially-varying Gaussian noise, we proposed a two-phase (including filtering and diffusing phases) image dejittering approach. In the filtering phase, outliers posed by jitters around edges are inspected by median filters. In the diffusing phase, structure tensor based anisotropic diffusion is exploited to reduce the perturbations in piecewise smooth image regions. Upon the spectral decomposition of structure tensor, the variational model in diffusing phase can be solved by some state-of-the-art optimization methods. Numerical simulations on synthetic and real jittery data demonstrate the compelling performance of the proposed approach. The Matlab source codes of the proposed approach are available at the repositories of https://github.com/WenxingZhang.

从空间变化混合噪声去除角度看图像抖动问题
抖动图像在视觉上表现为边缘锯齿状和失去连贯性。由于抖动数据中无处不在的模糊和/或噪声,解决图像去抖动问题具有挑战性。本文从空间变化混合噪声去除的角度,致力于像素抖动(可能模糊)图像的恢复。通过将抖动图像视为理想图像与离群值和空间变化高斯噪声的破坏,我们提出了一种两阶段(包括过滤和扩散阶段)图像去抖动方法。在滤波阶段,通过中值滤波器检查边缘抖动造成的异常值。在扩散阶段,利用基于结构张量的各向异性扩散来减少片状平滑图像区域的扰动。在对结构张量进行频谱分解后,扩散阶段的变分模型可以通过一些最先进的优化方法来解决。在合成和真实抖动数据上进行的数值模拟证明了所提方法的卓越性能。该方法的 Matlab 源代码可从 https://github.com/WenxingZhang 的资源库中获取。
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来源期刊
Signal Processing
Signal Processing 工程技术-工程:电子与电气
CiteScore
9.20
自引率
9.10%
发文量
309
审稿时长
41 days
期刊介绍: Signal Processing incorporates all aspects of the theory and practice of signal processing. It features original research work, tutorial and review articles, and accounts of practical developments. It is intended for a rapid dissemination of knowledge and experience to engineers and scientists working in the research, development or practical application of signal processing. Subject areas covered by the journal include: Signal Theory; Stochastic Processes; Detection and Estimation; Spectral Analysis; Filtering; Signal Processing Systems; Software Developments; Image Processing; Pattern Recognition; Optical Signal Processing; Digital Signal Processing; Multi-dimensional Signal Processing; Communication Signal Processing; Biomedical Signal Processing; Geophysical and Astrophysical Signal Processing; Earth Resources Signal Processing; Acoustic and Vibration Signal Processing; Data Processing; Remote Sensing; Signal Processing Technology; Radar Signal Processing; Sonar Signal Processing; Industrial Applications; New Applications.
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