The paper benchmarks two strategies for blind image restoration around a fixed image signal processor (ISP): restoring in the RAW domain before the ISP (pre‑ISP) and restoring in the sRGB domain after the ISP (post‑ISP). Across four smartphone groups, two learned ISPs, and three degradation regimes (noise, blur, and combined noise‑blur), the study finds that RAW restoration generally outperforms generic RGB restoration, but RGB models trained with ISP‑aware supervision achieve the best overall performance. The authors emphasize that restoration performance depends heavily on how well the restoration model aligns with the imaging pipeline, and they recommend reporting restoration placement and ISP‑aware supervision as key experimental factors.
By Zihao Lu, Radu Timofte, Marcos V. Conde
arXiv:2609.27274v1 Announce Type: new
Abstract: Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow...
By Tao Zhang, Peixian Su, Xingyu Gao, Yunhao Zou, Yu Lu, Zunjie Zhu, Bolun Zheng, Ying Fu, Chenggang Yan
arXiv:2609.18302v1 Announce Type: new
Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect sp...
By Tailai Chen, Xiaotong Luo, Yuan Gao, Xin Jin, Wenjun Zeng
Due to the limited dynamic range of conventional image sensors, captured low dynamic range (LDR) video often suffers from highlight clipping and shadow detail loss, making high-quality high dynamic ra...
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal degrades; consequently, for low-light conditions, robust denoising is especially vital for high-quality results.
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
The paper introduces LLPR, a framework that integrates location-aware learning and physics-based reconstruction to remove raindrops from single images. It replaces costly preprocessing masks with a learnable branch that can be discarded during inference, and reconstructs the background by first estimating a transparency matrix and raindrop layer using a physical model. The authors also present a new real-world dataset and show that LLPR outperforms existing state‑of‑the‑art methods.
By Zewei He, Xingyu Liu, Xing Luo, Guizhong Fu, Zixuan Chen, Yu Chen, Jinlei Li, Zhe-Ming Lu
arXiv:2608.21847v1 Announce Type: new
Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport c...
By Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
By Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng
arXiv:2605. 13258v2 Announce Type: replace-cross Abstract: In this work, we present our winning solution for the 8th UG2+ Challenge (CVPR 2026) Track 1: Image Restoration under All-weather Conditions.
By Youwei Pan, Leilei Cao, Yingfang Zhu, Fengjie Zhu
Weather-Conditioned Depth Anything (DA‑W) is a new framework that enhances monocular depth estimation models, like the Depth Anything series, to perform robustly under adverse weather conditions such as fog, rain, snow, and low‑light. It achieves this by disentangling style from content: a Style Filter extracts weather‑specific embeddings from a curated mix of real and synthetic degradation data, which are then injected into the backbone via a lightweight, zero‑initialized adapter. The adapter is trained with pseudo‑label distillation and alignment, enabling a single unified model to adapt to diverse weather scenarios while preserving its generalization on clean data, and it achieves state‑of‑the‑art performance with an average 3.7% improvement in AbsRel on weather benchmarks.
By Zhaoming Xu, Chan-Wei Hu, Kuan-Ru Huang, Zihao Zhu, Renjie Li, Yang Zhou, Zhengzhong Tu
arXiv:2406. 07435v2 Announce Type: replace-cross Abstract: Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted images, respectively.
By Shashank Agnihotri, Julia Grabinski, Janis Keuper, Margret Keuper