arXiv Computer Vision
Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing
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arXiv Computer Vision
Aug 25
BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement
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...
arXiv AI
1d ago
Paint-Anything: Unified Any-Color Control for Image Generation and Editing
Paint-Anything introduces a unified hex-prompt interface that allows users to specify any 24‑bit hex color for both image generation and editing. The method trains on a new Paint‑500K dataset created from real images with object grounding, perceptual color labeling, and editing‑pair synthesis, and supplements this with pure‑color anchors to address shadow‑induced color inaccuracies. Evaluated on the newly proposed Any Color Benchmark (ACBench), Paint‑Anything achieves significant improvements over the base FLUX.2‑4B model, boosting T2I and editing scores by 85.3 % and 28.3 % respectively, and outperforms competing methods on the CompColor metric.
arXiv Computer Vision
Sep 3
Benchmarking RAW and RGB Restoration in Image Signal Processors
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.
Hugging Face Trending Papers
Jul 13
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
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.
Hugging Face Trending Papers
Jul 30
BlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can be time-consuming and not always practical.
arXiv Computer Vision
Sep 1
PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution
arXiv:2608.30782v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...