改进梯度算法，让模糊图像恢复更清晰更快。
An Improved Optimal Proximal Gradient Algorithm for Non-Blind Image Deblurring
- 基于优化梯度与加权矩阵，提升收敛效率。
- 在L1和总变差正则下，PSNR与SSIM均更高。
- 适合需要快速高质去模糊的图像处理场景。
图像去模糊是图像处理的核心研究方向，对提升图像质量及视觉呈现具有重要意义。本文针对已知模糊核的非盲去模糊问题，提出一种改进的最优近端梯度算法（IOptISTA），该方法在最优梯度法基础上引入加权矩阵，有效解决优化问题。分别在$l_1$范数和总变差（total variation）正则化条件下进行数值实验。结果表明，相比现有方法，所提算法在相同条件下显著提升了峰值信噪比（PSNR）和结构相似性（SSIM），同时降低了收敛容差，验证了其有效性与优越性。
原文摘要 · Abstract (English)
Image deblurring remains a central research area within image processing, critical for its role in enhancing image quality and facilitating clearer visual representations across diverse applications. This paper tackles the optimization problem of image deblurring, assuming a known blurring kernel. We introduce an improved optimal proximal gradient algorithm (IOptISTA), which builds upon the optimal gradient method and a weighting matrix, to efficiently address the non-blind image deblurring problem. Based on two regularization cases, namely the $l_1$ norm and total variation norm, we perform numerical experiments to assess the performance of our proposed algorithm. The results indicate that our algorithm yields enhanced PSNR and SSIM values, as well as a reduced tolerance, compared to existing methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。