Saturation-Aware Space-Variant Blind Image Deblurring
For computer vision researchers, this work addresses the challenging problem of deblurring images with saturated pixels, common in high dynamic range and low-light conditions, offering improved performance without ringing artifacts.
This paper introduces a saturation-aware space-variant blind image deblurring framework that segments images based on blur intensity and saturation, uses a pre-estimated Light Spread Function to reduce stray light, and estimates true radiance of saturated regions via dark channel prior. The method improves deblurring outcomes on synthetic and real-world datasets, outperforming state-of-the-art methods.
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.