Improved restoration algorithm for weakly blurred and strongly noisy image

Qianshun Liu; Guo Xia; Haiyang Zhou; Jian Bai; Feihong Yu

doi:10.1117/12.2197673

8 October 2015 Improved restoration algorithm for weakly blurred and strongly noisy image

Qianshun Liu, Guo Xia, Haiyang Zhou, Jian Bai, Feihong Yu

Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 96750J (2015) https://doi.org/10.1117/12.2197673
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China

Abstract

In real applications, such as consumer digital imaging, it is very common to record weakly blurred and strongly noisy images. Recently, a state-of-art algorithm named geometric locally adaptive sharpening (GLAS) has been proposed. By capturing local image structure, it can effectively combine denoising and sharpening together. However, there still exist two problems in the practice. On one hand, two hard thresholds have to be constantly adjusted with different images so as not to produce over-sharpening artifacts. On the other hand, the smoothing parameter must be manually set precisely. Otherwise, it will seriously magnify the noise. However, these parameters have to be set in advance and totally empirically. In a practical application, this is difficult to achieve. Thus, it is not easy to use and not smart enough. In an effort to improve the restoration effect of this situation by way of GLAS, an improved GLAS (IGLAS) algorithm by introducing the local phase coherence sharpening Index (LPCSI) metric is proposed in this paper. With the help of LPCSI metric, the two hard thresholds can be fixed at constant values for all images. Compared to the original method, the thresholds in our new algorithm no longer need to change with different images. Based on our proposed IGLAS, its automatic version is also developed in order to compensate for the disadvantages of manual intervention. Simulated and real experimental results show that the proposed algorithm can not only obtain better performances compared with the original method, but it is very easy to apply.

Citation Download Citation

Qianshun Liu, Guo Xia, Haiyang Zhou, Jian Bai, and Feihong Yu "Improved restoration algorithm for weakly blurred and strongly noisy image", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 96750J (8 October 2015); https://doi.org/10.1117/12.2197673

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KEYWORDS

Image restoration

Digital imaging

Image processing

Cameras

Computer simulations

Databases

Denoising

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