arXiv Computer Vision
Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing
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arXiv Computer Vision
6d ago
PREEN: Exemplar-Based Color Style Transfer with Smartphone Editor Presets
PREEN (Preset Regression via an Exemplar-based Editor Network) performs color style transfer by predicting a 29‑parameter preset for a deterministic image editor, rather than generating a raster image. It uses a style‑transfer teacher to create pseudo‑presets, then trains a CLIP‑conditioned regressor refined with LoRA and a CIELAB loss. On the PST50 benchmark, PREEN outperforms baselines in pixel‑ and perceptual‑fidelity, runs in sub‑second on‑device latency, and offers a single inference‑time strength knob that smoothly transitions from default to vivid presets.
arXiv Computer Vision
Oct 15
Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation
arXiv:2610.15806v2 Announce Type: replace Abstract: Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods...
arXiv Computer Vision
Oct 22
$\mathbf{R}^3$: Reconstruction, Raw, and Rain: Deraining Directly in the Bayer Domain
arXiv:2509.24022v3 Announce Type: replace Abstract: Image reconstruction from corrupted images is crucial across many domains. Most reconstruction networks are trained on post-ISP sRGB images, even t...
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 Computer Vision
Oct 27
From Sentinel-2 to 1-m Imagery: Real-World $\times 10$ Cross-Sensor Super-Resolution via Visual Autoregressive Modeling
arXiv:2610.34742v1 Announce Type: new Abstract: Reconstructing 1-m imagery from Sentinel-2 combines two difficulties. The input and the reference are observed by different satellites, which differ in...
arXiv AI
Sep 18
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.