1 November 2010 Region-based adaptive anisotropic diffusion for image enhancement and denoising
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Abstract
A novel region-based adaptive anisotropic diffusion (RAAD) is presented for image enhancement and denoising. The main idea of this algorithm is to perform the region-based adaptive segmentation. To this end, we use the eigenvalue difference of the structure tensor of each pixel to classify an image into homogeneous detail, and edge regions. According to the different types of regions, a variable weight is incorporated into the anisotropic diffusion partial differential equation for compromising the forward and backward diffusion, so that our algorithm can adaptively encourage strong smoothing in homogeneous regions and suitable sharpening in detail and edge regions. Furthermore, we present an adaptive gradient threshold selection strategy. We suggest that the optimal gradient threshold should be estimated as the mean of local intensity differences on the homogeneous regions. In addition, we modify the anisotropic diffusion discrete scheme by taking into account edge orientations. We believe our algorithm to be a novel mechanism for image enhancement and denoising. Qualitative experiments, based on various general digital images and several T1- and T2-weighted magnetic resonance simulated images, show significant improvements when the RAAD algorithm is used versus the existing anisotropic diffusion and the previous forward and backward diffusion algorithms for enhancing edge features and improving image contrast. Quantitative analyses, based on peak signal-to-noise ratio, the universal image quality index, and the structural similarity confirm the superiority of the proposed algorithm.
© (2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Yi Wang, Yi Wang, Ruiqing Niu, Ruiqing Niu, Liangpei Zhang, Liangpei Zhang, Huanfeng Shen, Huanfeng Shen, } "Region-based adaptive anisotropic diffusion for image enhancement and denoising," Optical Engineering 49(11), 117007 (1 November 2010). https://doi.org/10.1117/1.3517741 . Submission:
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