Blind Deconvolution of Binary and Pattern Images with Pixel Intensity Constraints and Sparse Gradient Prior

2026-09-19

AI Summary

本文针对二值和图案图像的盲去卷积问题，提出了一种结合像素强度约束与梯度稀疏正则化的方法，实验表明该方法在视觉质量和量化指标上优于现有技术。

Abstract

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.

Problem

• Blind Image Deconvolution
• Pixel Intensity Constraints
• Sparse Gradient Prior
• Binary Images
• Pattern Images

Innovation

• pixel intensity constraint
• gradient sparsity
• blind deconvolution
• binary and pattern images