Saturation-Aware Space-Variant Blind Image Deblurring

• URL: http://arxiv.org/abs/2604.16200v1
• Date: Fri, 17 Apr 2026 16:10:19 GMT
• Title: Saturation-Aware Space-Variant Blind Image Deblurring
• Abstract summary: This paper presents a novel saturation aware space variant blind image deblurring framework to address challenges posed by saturated pixels in deblurring under high dynamic range and low light conditions.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Simultaneous Enhancement and Noise Suppression under Complex Illumination Conditions [18.76552485320789]
  We propose a novel framework for simultaneous enhancement and noise suppression under complex illumination conditions.<n>The proposed method is evaluated on real-world datasets obtained from practical applications.
  arXiv  Detail & Related papers  (2025-12-09T09:04:26Z)
• BokehDiff: Neural Lens Blur with One-Step Diffusion [62.59018200914645]
  We introduce BokehDiff, a lens blur rendering method that achieves physically accurate and visually appealing outcomes.<n>Our method employs a physics-inspired self-attention module that aligns with the image formation process.<n>We adapt the diffusion model to the one-step inference scheme without introducing additional noise, and achieve results of high quality and fidelity.
  arXiv  Detail & Related papers  (2025-07-24T03:23:19Z)
• Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement [3.174882428337821]
  We propose a variational method for low-light image enhancement based on the Retinex decomposition.<n>A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition.<n>We extend the model by introducing its deep unfolding counterpart, in which the operators are replaced with learnable networks.
  arXiv  Detail & Related papers  (2025-04-10T14:48:26Z)
• Inhomogeneous illumination image enhancement under ex-tremely low visibility condition [3.534798835599242]
  Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition obscured, thereby hindering conventional image processing methods. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (F) to enhance only vital signal information. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.
  arXiv  Detail & Related papers  (2024-04-26T16:09:42Z)
• Global Structure-Aware Diffusion Process for Low-Light Image Enhancement [64.69154776202694]
  This paper studies a diffusion-based framework to address the low-light image enhancement problem. We advocate for the regularization of its inherent ODE-trajectory. Experimental evaluations reveal that the proposed framework attains distinguished performance in low-light enhancement.
  arXiv  Detail & Related papers  (2023-10-26T17:01:52Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• TensoIR: Tensorial Inverse Rendering [51.57268311847087]
  TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields. TensoRF is a state-of-the-art approach for radiance field modeling.
  arXiv  Detail & Related papers  (2023-04-24T21:39:13Z)
• High Dynamic Range and Super-Resolution from Raw Image Bursts [52.341483902624006]
  This paper introduces the first approach to reconstruct high-resolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration. Experiments demonstrate its excellent performance with super-resolution factors of up to $times 4$ on real photographs taken in the wild with hand-held cameras.
  arXiv  Detail & Related papers  (2022-07-29T13:31:28Z)
• Deep Bilateral Retinex for Low-Light Image Enhancement [96.15991198417552]
  Low-light images suffer from poor visibility caused by low contrast, color distortion and measurement noise. This paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions.
  arXiv  Detail & Related papers  (2020-07-04T06:26:44Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.