Saturation-Aware Space-Variant Blind Image Deblurring

TL;DR

This paper introduces a saturation-aware space-variant blind image deblurring framework that effectively handles saturated pixels in high dynamic range and low light conditions, improving deblurring quality.

Contribution

The proposed method uniquely segments images based on blur and saturation, estimates true radiance using the dark channel prior, and mitigates stray light effects, advancing blind deblurring techniques.

Findings

Improves deblurring results on synthetic and real datasets.

Reduces artifacts like ringing in saturated regions.

Outperforms existing saturation-aware and general deblurring methods.

Abstract

This paper presents a novel saturation aware space variant blind image deblurring framework designed to address challenges posed by saturated pixels in deblurring under high dynamic range and low light conditions. The proposed approach effectively segments the image based on blur intensity and proximity to saturation, leveraging a pre estimated Light Spread Function to mitigate stray light effects. By accurately estimating the true radiance of saturated regions using the dark channel prior, our method enhances the deblurring process without introducing artifacts like ringing. Experimental evaluations on both synthetic and real world datasets demonstrate that the framework improves deblurring outcomes across various scenarios showcasing superior performance compared to state of the art saturation-aware and general purpose methods. This adaptability highlights the framework potential integration with existing and emerging blind image deblurring techniques.