A novel spatially adaptive guide-filter total variation (SAGFTV) regularization for image restoration

Hao Fang; Qian Li; Zhenghua Huang

doi:10.1117/12.2203594

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14 December 2015 A novel spatially adaptive guide-filter total variation (SAGFTV) regularization for image restoration

Hao Fang, Qian Li, Zhenghua Huang

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Proceedings Volume 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 981510 (2015) https://doi.org/10.1117/12.2203594
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

Abstract

Denoising algorithms based on gradient dependent energy functionals, such as Perona-Malik, total variation and adaptive total variation denoising, modify images towards piecewise constant functions. Although edge sharpness and location is well preserved, important information, encoded in image features like textures or certain details, is often compromised in the process of denoising. In this paper, We propose a novel Spatially Adaptive Guide-Filtering Total Variation (SAGFTV) regularization with image restoration algorithm for denoising images. The guide-filter is extended to the variational formulations of imaging problem, and the spatially adaptive operator can easily distinguish flat areas from texture areas. Our simulating experiments show the improvement of peak signal noise ratio (PSNR), root mean square error (RMSE) and structure similarity increment measurement (SSIM) over other prior algorithms. The results of both simulating and practical experiments are more appealing visually. This type of processing can be used for a variety of tasks in PDE-based image processing and computer vision, and is stable and meaningful from a mathematical viewpoint.

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Hao Fang, Qian Li, and Zhenghua Huang "A novel spatially adaptive guide-filter total variation (SAGFTV) regularization for image restoration", Proc. SPIE 9815, MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 981510 (14 December 2015); https://doi.org/10.1117/12.2203594

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