7 papers
Leveraging Color Naming for Image Enhancement
David Serrano-Lozano, Luis Herranz, Michael S. Brown +1
Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editin…
GLUT: 3D Gaussian Lookup Table for Continuous Color Transformation
Danna Xue, David Serrano-Lozano, Shaolin Su +1
3D Lookup Tables (3D LUTs) are widely used for color mapping, but their grid-based representation requires discretizing the RGB space, leading to a capacity-memory trade-off that b…
SyncLight: Single-Edit Multi-View Relighting
David Serrano-Lozano, Anand Bhattad, Luis Herranz +2
We present SyncLight, a method to enable consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioned on a single view. While…
Evaluating Low-Light Image Enhancement Across Multiple Intensity Levels
Maria Pilligua, David Serrano-Lozano, Pai Peng +3
Imaging in low-light environments is challenging due to reduced scene radiance, which leads to elevated sensor noise and reduced color saturation. Most learning-based low-light enh…
Bridging the Perception Gap in Image Super-Resolution Evaluation
Shaolin Su, Josep M. Rocafort, Danna Xue +3
As super-resolution (SR) techniques advance, we observe a growing distrust of evaluation metrics in recent SR research. An inconsistency often emerges between certain evaluation cr…
Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
David Serrano-Lozano, Aditya Arora, Luis Herranz +3
White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural…