5 citations · 8 across the 14 of their papers we have counts for
20 papers
On Color Alignment in VAE Latent Spaces and Its Applications
Julian D. Santamaria, Kai Wang, Jesús Malo +2
Variational autoencoders (VAEs) are a key part of modern text-to-image models, which generate images within their latent space. VAEs are known to disentangle the main factors of va…
UniSlider: Perceptually Uniform Sliders for Continuous Image Editing
David Serrano-Lozano, Duygu Ceylan, Yannick Hold-Geoffroy +3
Sliders provide an intuitive interface for continuous image editing. In current generative approaches, however, the slider is simply a rescaling of the method's strength parameter,…
Shedding Light: A Benchmark for Evaluating Lighting Understanding in Generative Image Models
Justine Giroux, Jack Oliver Hilliard, Yannick Hold-Geoffroy +2
Accurate modelling of illumination is central to realistic image synthesis and scene understanding. Yet, there is little exploration into whether image generative models are good a…
Albedo Estimation via Latent Bridge Matching
Carme Corbi, David Serrano-Lozano, Javier Vazquez-Corral +1
Recent advances in Intrinsic Image Decomposition (IID) have increasingly relied on generative models. However, progress remains limited by three key challenges: (a) insufficient ph…
Do Vision Encoders Exhibit Human-like Color Thresholds?
Engy Ehab, Pablo Hernández-Cámara, Nahla Belal +3
Understanding and characterizing human color perception is a longstanding research goal. One of the most traditional approaches is looking for the human color discrimination thresh…
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…
SIMI: Self-information Mining Network for Low-light Image Enhancement
Xuanshuo Fu, Lei Kang, Javier Vazquez-Corral
Poor lighting conditions significantly impact image quality, posing substantial challenges for image editing and visualization. Many existing enhancement methods aim at proposing c…
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…
NamedCurves: Learned Image Enhancement via Color Naming
David Serrano-Lozano, Luis Herranz, Michael S. Brown +1
A popular method for enhancing images involves learning the style of a professional photo editor using pairs of training images comprised of the original input with the editor-enha…
ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement
Muhammad Atif Butt, Kai Wang, Javier Vazquez-Corral +1
Text-to-Image (T2I) generation has made significant advancements with the advent of diffusion models. These models exhibit remarkable abilities to produce images based on textual p…
LayeredDoc: Domain Adaptive Document Restoration with a Layer Separation Approach
Maria Pilligua, Nil Biescas, Javier Vazquez-Corral +3
The rapid evolution of intelligent document processing systems demands robust solutions that adapt to diverse domains without extensive retraining. Traditional methods often falter…
Towards a Perceptual Evaluation Framework for Lighting Estimation
Justine Giroux, Mohammad Reza Karimi Dastjerdi, Yannick Hold-Geoffroy +2
Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable a…
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement
Marcos V. Conde, Javier Vazquez-Corral, Michael S. Brown +1
3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipelin…
Perceptual Image Enhancement for Smartphone Real-Time Applications
Marcos V. Conde, Florin Vasluianu, Javier Vazquez-Corral +1
Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartp…
Matching visual induction effects on screens of different size
Trevor D. Canham, Javier Vazquez-Corral, Elise Mathieu +1
In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phe…
Visual Illusions Also Deceive Convolutional Neural Networks: Analysis and Implications
A. Gomez-Villa, A. Martín, J. Vazquez-Corral +2
Visual illusions allow researchers to devise and test new models of visual perception. Here we show that artificial neural networks trained for basic visual tasks in natural images…
Synthesizing Visual Illusions Using Generative Adversarial Networks
Alexander Gomez-Villa, Adrian Martín, Javier Vazquez-Corral +2
Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system. In this work we intro…
Convolutional Neural Networks Deceived by Visual Illusions
Alexander Gomez-Villa, Adrián Martín, Javier Vazquez-Corral +1
Visual illusions teach us that what we see is not always what it is represented in the physical world. Its special nature make them a fascinating tool to test and validate any new…
On the Duality Between Retinex and Image Dehazing
Adrian Galdran, Aitor Alvarez-Gila, Alessandro Bria +2
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Hum…