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cs.CV2026

OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression

Chengyan Deng, Pengbin Yu, Zhentao Chen +6

Diffusion-based real-world image super-resolution (Real-ISR) has achieved remarkable perceptual quality; however, directly super-resolving images to 4K remains limited by extreme m…

cs.CV2026

UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement

Jingwei Yang, Ruoxi Wu, Wei Shen +4

Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, leading to reference-content l…

cs.CV2026

Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

Yuhai Deng, Huimin She, Wei Shen +4

Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet dat…

cs.CV2026

RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution

Yushuai Song, Weize Quan, Weining Wang +8

Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perceptio…

cs.CV2024

Learning Subject-Aware Cropping by Outpainting Professional Photos

James Hong, Lu Yuan, Michaël Gharbi +2

How to frame (or crop) a photo often depends on the image subject and its context; e.g., a human portrait. Recent works have defined the subject-aware image cropping task as a nuan…