Blind image noise assessment based on local phase coherence

Lin Wang; Xiao Li; Yanyun Zhang; Jiaobo Gao; Mingyin Jiao

doi:10.1117/12.2176894

21 May 2015 Blind image noise assessment based on local phase coherence

Lin Wang, Xiao Li, Yanyun Zhang, Jiaobo Gao, Mingyin Jiao

Proceedings Volume 9501, Satellite Data Compression, Communications, and Processing XI; 950117 (2015) https://doi.org/10.1117/12.2176894
Event: SPIE Sensing Technology + Applications, 2015, Baltimore, MD, United States

Abstract

Remote Sensing Image can be degraded by a variety of causes during acquisition, transmission, compression, storage and reconstruction. Noise is one of the most important degradation factors. Quantifying its impact on the image may be useful for applications such as improving the acquisition system and thus the quality of the produced images. Objective Image Quality Measure (IQA) methods can be classified by whether a reference image, representing the original signal exists. In the case of remote sensing, the ideal un-degraded image is not available. No-reference (NR) method is required to blindly assess the image quality. In this paper, a new no-reference algorithm is proposed to quantify noise based on local phase coherence (LPC). This algorithm assumes that the input image is contaminated by additive zero mean Gaussian noise. Firstly, a LPC map of degraded image is constructed and the image edge is extracted by modifying the noise threshold. Secondly, the edge is removed from the LPC map. Then, the noise level can be quantified by the remaining noise information and little “residual” information of the LPC map. Experiment results show that the proposed algorithm correlates well with subjective quality evaluations and has high estimation accuracy especially for Gaussian noise-infected images.

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Lin Wang, Xiao Li, Yanyun Zhang, Jiaobo Gao, and Mingyin Jiao "Blind image noise assessment based on local phase coherence", Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 950117 (21 May 2015); https://doi.org/10.1117/12.2176894

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KEYWORDS

Image quality

Remote sensing

Image filtering

Coherence (optics)

Wavelets

Image analysis

Image compression

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