3 papers
cs.CV2026
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
Haizhen Xie, Kunpeng Du, Qiangyu Yan +5
Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. While T2I models have traditionally…
cs.CV2025
OS-DiffVSR: Towards One-step Latent Diffusion Model for High-detailed Real-world Video Super-Resolution
Hanting Li, Huaao Tang, Jianhong Han +5
Recently, latent diffusion models has demonstrated promising performance in real-world video super-resolution (VSR) task, which can reconstruct high-quality videos from distorted l…
cs.CV2024
Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation
Yuchuan Tian, Jianhong Han, Hanting Chen +5
Due to the unaffordable size and intensive computation costs of low-level vision models, All-in-One models that are designed to address a handful of low-level vision tasks simultan…