解决高动态与弱光下图像去模糊中的饱和像素问题。
Saturation-Aware Space-Variant Blind Image Deblurring
- 根据模糊程度和接近饱和度分割图像，利用光照扩散函数抑制杂散光。
- 通过暗通道先验准确恢复饱和区域真实辐射值，避免伪影产生。
- 适用于复杂光照场景，适合需要高保真去模糊的研究者。
本文提出一种新颖的饱和感知空间变异性盲图像去模糊框架，用于应对高动态范围和低光条件下饱和像素带来的挑战。该方法基于模糊强度与接近饱和度对图像进行有效分割，结合预先估计的光照扩散函数以减轻杂散光影响。通过暗通道先验准确估计饱和区域的真实辐射值，提升了去模糊效果，且未引入如振铃等伪影。在合成与真实世界数据集上的实验表明，该框架在多种场景下均优于现有饱和感知及通用去模糊方法，展现出与现有及新兴盲去模糊技术融合的潜力。
原文摘要 · Abstract (English)
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.
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