10 citations · 16 across the 20 of their papers we have counts for
24 papers
TGSR: Text-Grounded Diffusion for Faithful Image Super-Resolution
Zhiming Hu, Ran Zhang, Angela Ye +7
Diffusion models have achieved remarkable success in image super-resolution (SR) by leveraging strong generative priors to enhance perceptual quality. However, for textual content,…
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
Mohammad Mohammadi, Sina Honari, Stavros Tsogkas +6
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 d…
AVIS: Adaptive Test-Time Scaling for Vision-Language Models
Ahmadreza Jeddi, Minh Ngoc Le, Amirhossein Kazerouni +8
Modern Vision-Language Models (VLMs) benefit from chain-of-thought prompting and test-time scaling, but these gains often come with prohibitive inference cost due to large visual c…
BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors
Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej +8
Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal r…
Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration
Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong +6
Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods…
RawGen: Learning Camera Raw Image Generation
Dongyoung Kim, Junyong Lee, Abhijith Punnappurath +4
Cameras capture scene-referred linear raw images, which are processed by onboard image signal processors (ISPs) into display-referred 8-bit sRGB outputs. Although raw data is more…