Image denoising based on seperable total variation model

被引：0
作者：
机构：
[1] Faculty of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an
来源：
Wang, Bin | 1600年 / Chinese Optical Society卷 / 43期
关键词：
Convex optimazition; Image denoising; Random noise; Separable; Total variation;
D O I：
10.3788/gzxb20144309.0910003
中图分类号：
学科分类号：
摘要：
Based on traditional discrete total variation model, we established the separable total variation model exploiting low-dimensional projection; Combining with Frobenius norm and the convexity of image, we proposed a method that rooted in convex optimization to solve the separable discrete total variation problem, which can be applied into image denoising. Simulation results show that, with the ability of effectively keeping profile and details, the peak signal to noise ratio of 256×256 size image after denoising can reach 28.5 dB while the variance of random noise is 0.1, thus illustrating the good performance at the removal of random noise. By revising the numbers of iterations, the relationship between speed and accuracy can be balanced with considerable flexibility, thus adjusting to different denoising requirements.
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