Bhatt Rajesh, Naik Naren, Subramanian Venkatesh K
IEEE Trans Image Process. 2021;30:2611-2626. doi: 10.1109/TIP.2021.3053369. Epub 2021 Feb 5.
In image processing, it is well known that mean square error criteria is perceptually inadequate. Consequently, image quality assessment (IQA) has emerged as a new branch to overcome this issue, and this has led to the discovery of one of the most popular perceptual measures, namely, the structural similarity index (SSIM). This measure is mathematically simple, yet powerful enough to express the quality of an image. Therefore, it is natural to deploy SSIM in model based applications, such as denoising, restoration, classification, etc. However, the non-convex nature of this measure makes this task difficult. Our attempt in this work is to discuss problems associated with its convex program and take remedial action in the process of obtaining a generalized convex framework. The obtained framework has been seen as a component of an alternative learning scheme for the case of a regularized linear model. Subsequently, we develop a relevant dictionary learning module as a part of alternative learning. This alternative learning scheme with sparsity prior is finally used in denoising and deblurring applications. To further boost the performance, an iterative scheme is developed based on the statistical nature of added noise. Experiments on image denoising and deblurring validate the effectiveness of the proposed scheme. Furthermore, it has been shown that the proposed framework achieves highly competitive performance with respect to other schemes in literature and performs better in natural images in terms of SSIM and visual inspection.
在图像处理中，众所周知，均方误差准则在感知上是不充分的。因此，图像质量评估（IQA）作为一个新的分支出现，以克服这个问题，这导致了发现一种最流行的感知度量，即结构相似性指数（SSIM）。这种度量在数学上很简单，但强大到足以表达图像的质量。因此，在基于模型的应用中部署SSIM是很自然的，比如去噪、恢复、分类等。然而，这种度量的非凸性质使得这项任务变得困难。我们在这项工作中的尝试是讨论与其凸规划相关的问题，并在获得广义凸框架的过程中采取补救措施。所获得的框架被视为正则化线性模型情况下替代学习方案的一个组成部分。随后，我们开发了一个相关的字典学习模块作为替代学习的一部分。这种具有稀疏先验的替代学习方案最终用于去噪和去模糊应用。为了进一步提高性能，基于添加噪声的统计特性开发了一种迭代方案。图像去噪和去模糊实验验证了所提方案的有效性。此外，已经表明，所提框架相对于文献中的其他方案具有极具竞争力的性能，并且在自然图像的结构相似性指数和视觉检查方面表现更好。
