发表机构
Beijing Normal University; Anhui University; University of Electronic Science and Technology of China; Northeast Normal University（北京师范大学; 安徽大学; 电子科技大学; 东北师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对条形码、文本等像素取值受限的图像，提出结合像素强度约束与梯度稀疏正则化的统一盲反卷积框架，实验证明其在视觉与定量指标上优于现有方法。
AI 中文摘要
盲图像反卷积（BID）是成像科学领域的一个突出研究课题，因其具有重要的实际应用价值。大多数现有的基于模型的BID方法专注于自然图像，并结合关于底层图像和模糊核的适当先验知识。然而，对于某些类别的图像，如条形码、文本和图案，像素只能取非常有限的值，这一特定先验在文献中常常被忽视。在本文中，我们引入了一种新颖的像素强度约束来利用这一重要信息，从而提高这些专门图像类别的恢复性能。具体来说，我们提出了一个统一的框架，用于盲二元图像和图案图像反卷积，该框架同时结合了像素强度约束和梯度稀疏正则化器。数值实验表明，我们的方法优于许多现有的BID技术，在视觉质量和定量指标方面均取得了更优的结果。
英文摘要
Blind image deconvolution (BID) is a prominent research topic in the field of imaging sciences, given its significant practical applications. Most existing model-based BID methods focus on natural images, incorporating appropriate prior knowledge about both the underlying image and the blur kernel. However, for certain classes of images, such as barcodes, text, and patterns, pixels can only take very limited values, a specific prior that is often overlooked in the literature. In this article, we introduce a novel pixel intensity constraint to leverage this important information, improving recovery performance for these specialized image classes. Specifically, we propose a unified framework for blind binary and pattern image deconvolution that incorporates both the pixel intensity constraint and a gradient sparsity regularizer. Numerical experiments demonstrate that our method outperforms many existing BID techniques, achieving superior results in terms of both visual quality and quantitative metrics.
