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The advance of new technologies of this century has allowed us to develop tools for the medical process. Analysis of retinopathy images has become one of the main ways to detect some Non-Communicating Diseases, such as diabetes. The main problem with these kinds of images is that noise is added during the acquisition step. This noise may imply wrong medical resolutions and, hence, human life may be affected. Thus, improving the quality of the inputs before processing is an important task. In this paper, a comparison of state-of-the-art denoising methods as well as the K-SVD algorithm is presented. The evaluation was performed on synthetic images and SD-OCT images. Results show that K-SVD is a suitable tool for denoising SD-OCT images since it can decrease the noise while preserving edges.

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A Comparison of K-SVD with Traditional Denoising Algorithms

  • 会议论文

  • 发表日期：

    2026年

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  • Prudhvi Krishna Thandra

摘要

The advance of new technologies of this century has allowed us to develop tools for the medical process. Analysis of retinopathy images has become one of the main ways to detect some Non-Communicating Diseases, such as diabetes. The main problem with these kinds of images is that noise is added during the acquisition step. This noise may imply wrong medical resolutions and, hence, human life may be affected. Thus, improving the quality of the inputs before processing is an important task. In this paper, a comparison of state-of-the-art denoising methods as well as the K-SVD algorithm is presented. The evaluation was performed on synthetic images and SD-OCT images. Results show that K-SVD is a suitable tool for denoising SD-OCT images since it can decrease the noise while preserving edges.