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Proceedings Paper

Blind image noise assessment based on local phase coherence
Author(s): Lin Wang; Xiao Li; Yanyun Zhang; Jiaobo Gao; Mingyin Jiao
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Paper 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.

Paper Details

Date Published: 21 May 2015
PDF: 8 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 950117 (21 May 2015); doi: 10.1117/12.2176894
Show Author Affiliations
Lin Wang, Xidian Univ. (China)
Xiao Li, Xidian Univ. (China)
Yanyun Zhang, Xidian Univ. (China)
Jiaobo Gao, Xi’an Institute of Applied Optics North Electro-optics Group Co., Ltd. (China)
Mingyin Jiao, Xi’an Institute of Applied Optics North Electro-optics Group Co., Ltd. (China)

Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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