对比NL-means、K-SVD和BM3D三种去噪算法性能，为图像处理提供实用参考。
A Comparative Study of Image Denoising Algorithms
- 选取NL-means、K-SVD、BM3D三类先进算法进行系统对比
- 在自然图像、纹理图、合成图等多类数据集上验证效果
- 适合图像处理、医学影像等领域研究人员参考
随着信息产业的快速发展，数字图像作为人类大脑最易理解的数据形式，在图像处理、视觉计算、机器人、生物医学等多个领域扮演着关键角色。这些应用广泛存在于生物科学、医学、游戏技术、通信技术、统计科学、放射学及医疗检测等实时场景中。然而，数字图像在电子传输或相机采集过程中容易受到噪声干扰而退化。为解决此问题，文献中提出了多种鲁棒、低成本且快速的图像去噪算法。本研究系统考察了包括NL-means、K-SVD和BM3D在内的前沿去噪技术，使用标准图像、自然图像、纹理图像、合成图像及其他数据集进行测试，提供了详实可靠的对比结果，以支持高效算法选择。
原文摘要 · Abstract (English)
With the recent advancements in the field of information industry, critical data in the form of digital images is best understood by the human brain. Therefore, digital images play a significant part and backbone role in many areas such as image processing, vision computing, robotics, and bio-medical. Such use of digital images is practically implementable in various real-time scenarios like biological sciences, medicine, gaming technology, computer information and communication technology, data and statistical science, radiological sciences and medical imaging technology, and medical lab technology. However, when any digital image is sent electronically or captured via camera, it is likely to get corrupted or degraded by the available of degradation factors. To eradicate this problem, several image denoising algorithms have been proposed in the literature focusing on robust, low-cost and fast techniques to improve output performance. Consequently, in this research project, an earnest effort has been made to study various image denoising algorithms. A specific focus is given to the start-of-the-art techniques namely: NL-means, K-SVD, and BM3D. The standard images, natural images, texture images, synthetic images, and images from other datasets have been tested via these algorithms, and a detailed set of convincing results have been provided for efficient comparison.
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