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.

翻译：图像去模糊作为图像处理领域的核心研究方向，对于提升图像质量及在各类应用中实现更清晰的视觉呈现具有关键作用。本文针对已知模糊核条件下的图像去模糊优化问题展开研究。我们提出了一种改进的最优近端梯度算法（IOptISTA），该算法基于最优梯度法并结合加权矩阵，以高效解决非盲图像去模糊问题。基于$l_1$范数与全变差范数两种正则化情形，我们通过数值实验评估了所提算法的性能。结果表明，相较于现有方法，本算法能够获得更高的峰值信噪比与结构相似性指数，同时具有更低的容差阈值。