Fractional Total Variation Denoising Model Based on Adaptive Projection Algorithm
ZHANG Guimei1, SUN Xiaoxu1, LIU Jianxin2
1.Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition, Nanchang Hangkong University, Nanchang 330063 2.School of Mechanical Engineering, Xihua University, Chengdu 610039
Abstract：To preserve weak edges and texture details of an image during image denoising, a fractional order total variation denoising model is presented based on adaptive projection algorithm. Firstly, Grünwald-Letnikov fractional order differential is used as a substitute for the first order derivative in the regularization term of total variation model. Secondly, the image is projected to a total variation ball to handle the optimization problem. The image is divided into the texture area and the non-texture area according to the local information of the image, and thus soft threshold values can be calculated adaptively. Both theoretical analysis and experimental results show that the proposed method eliminates the block effect as well as preserves the texture details effectively for removing noise.
张桂梅，孙晓旭，刘建新. 基于自适应投影算法的分数阶全变分去噪模型*[J]. 模式识别与人工智能, 2016, 29(11): 1009-1018.
ZHANG Guimei, SUN Xiaoxu, LIU Jianxin. Fractional Total Variation Denoising Model Based on Adaptive Projection Algorithm. , 2016, 29(11): 1009-1018.
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2016, Vol. 29 
