4.3 Article

Image Stripe Noise Removal Based on Compressed Sensing

出版社

WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218001422540040

关键词

Stripe noise removal; compressed sensing; sparse representation; curvelet

资金

  1. National Natural Science Foundation of China [61374127]
  2. Postdoctoral Science Foundation of China [2019M651254]
  3. Youth Science Foundation of Northeast Petroleum University [2018QNL-49]

向作者/读者索取更多资源

This study proposes a method based on compressed sensing to remove stripe noise in the camera's imaging process. By establishing the measurement matrix of the image with stripe noise and defining the relationships between the corresponding coefficients of adjacent scales, the removal of stripe noise and preservation of image texture details are achieved.
The sensors or electronic components are vulnerable to interference in the camera's imaging process, usually leading to random directional stripes. Therefore, a method of stripe noise removal based on compressed sensing is proposed. First, the measurement matrix of the image with stripe noise is established, which makes the stripe images equivalent to the observation of the original image. Second, the relationships between the corresponding coefficients of adjacent scales are defined. On this basis, the bivariate threshold function is set in the curvelet sparse domain to represent the features of images. Finally, the Landweber iteration algorithm of alternating convex projection and filtering operation is achieved. Furthermore, to accelerate the noise removal at the initial stage of iteration and preserve the image details later, the exponential threshold function is utilized. This method does not need many samples, which is different from the current deep learning method. The experimental results show that the proposed algorithm represents excellent performance in removing the stripes and preserving the texture details. In addition, the PSNR of the denoised image has been dramatically improved compared with similar algorithms.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据