From the 1 of 9 linked papers with an AI index.
9 papers
Why Low-Light Cameras Go Color Blind: Removing Color Bias in Raw Denoising
Mohammad Mohammadi, Sina Honari, Stavros Tsogkas +6
The paper introduces a calibration‑free approach for low‑light raw image denoising that first estimates and removes color bias caused by black‑level errors, improving color fidelit…
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…
Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks
Abhinav Kumar, Tristan Aumentado-Armstrong, Lazar Valkov +4
Gaussian Splatting has revolutionized the field of Novel View Synthesis (NVS) with faster training and real-time rendering. However, its reconstruction fidelity still trails behind…
Hallucination Score: Towards Mitigating Hallucinations in Generative Image Super-Resolution
Weiming Ren, Raghav Goyal, Zhiming Hu +3
Generative super-resolution (GSR) currently sets the state-of-the-art in terms of perceptual image quality, overcoming the "regression-to-the-mean" blur of prior non-generative mod…
Probabilistic Directed Distance Fields for Ray-Based Shape Representations
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
In modern computer vision, the optimal representation of 3D shape continues to be task-dependent. One fundamental operation applied to such representations is differentiable render…
Augmenting Perceptual Super-Resolution via Image Quality Predictors
Fengjia Zhang, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong +2
Super-resolution (SR), a classical inverse problem in computer vision, is inherently ill-posed, inducing a distribution of plausible solutions for every input. However, the desired…