1k citations · 1.1k across the 16 of their papers we have counts for
22 papers
UniRes: Universal Image Restoration for Complex Degradations
Mo Zhou, Keren Ye, Mauricio Delbracio +3
Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improv…
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
Keren Ye, Ignacio Garcia Dorado, Michalis Raptis +4
While recent advancements in Image Super-Resolution (SR) using diffusion models have shown promise in improving overall image quality, their application to scene text images has re…
Reference-Guided Identity Preserving Face Restoration
Mo Zhou, Keren Ye, Viraj Shah +5
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods o…
The Power of Context: How Multimodality Improves Image Super-Resolution
Kangfu Mei, Hossein Talebi, Mojtaba Ardakani +3
Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inp…
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio +3
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have s…
SPIRE: Semantic Prompt-Driven Image Restoration
Chenyang Qi, Zhengzhong Tu, Keren Ye +4
Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement. However, it still remains…
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
Kangfu Mei, Mauricio Delbracio, Hossein Talebi +3
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement, restoration,…
MULLER: Multilayer Laplacian Resizer for Vision
Zhengzhong Tu, Peyman Milanfar, Hossein Talebi
Image resizing operation is a fundamental preprocessing module in modern computer vision. Throughout the deep learning revolution, researchers have overlooked the potential of alte…
Multiscale Structure Guided Diffusion for Image Deblurring
Mengwei Ren, Mauricio Delbracio, Hossein Talebi +2
Diffusion Probabilistic Models (DPMs) have recently been employed for image deblurring, formulated as an image-conditioned generation process that maps Gaussian noise to the high-q…
Soft Diffusion: Score Matching for General Corruptions
Giannis Daras, Mauricio Delbracio, Hossein Talebi +2
We define a broader family of corruption processes that generalizes previously known diffusion models. To reverse these general diffusions, we propose a new objective called Soft S…
MaxViT: Multi-Axis Vision Transformer
Zhengzhong Tu, Hossein Talebi, Han Zhang +4
Transformers have recently gained significant attention in the computer vision community. However, the lack of scalability of self-attention mechanisms with respect to image size h…
MAXIM: Multi-Axis MLP for Image Processing
Zhengzhong Tu, Hossein Talebi, Han Zhang +4
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effe…
Deblurring via Stochastic Refinement
Jay Whang, Mauricio Delbracio, Hossein Talebi +3
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean ima…
Exponentiated Gradient Reweighting for Robust Training Under Label Noise and Beyond
Negin Majidi, Ehsan Amid, Hossein Talebi +1
Many learning tasks in machine learning can be viewed as taking a gradient step towards minimizing the average loss of a batch of examples in each training iteration. When noise is…
Learning to Resize Images for Computer Vision Tasks
Hossein Talebi, Peyman Milanfar
For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image…
Deep Perceptual Image Quality Assessment for Compression
Juan Carlos Mier, Eddie Huang, Hossein Talebi +2
Lossy Image compression is necessary for efficient storage and transfer of data. Typically the trade-off between bit-rate and quality determines the optimal compression level. This…
Projected Distribution Loss for Image Enhancement
Mauricio Delbracio, Hossein Talebi, Peyman Milanfar
Features obtained from object recognition CNNs have been widely used for measuring perceptual similarities between images. Such differentiable metrics can be used as perceptual lea…
Rank-smoothed Pairwise Learning In Perceptual Quality Assessment
Hossein Talebi, Ehsan Amid, Peyman Milanfar +1
Conducting pairwise comparisons is a widely used approach in curating human perceptual preference data. Typically raters are instructed to make their choices according to a specifi…
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
Xiyang Luo, Hossein Talebi, Feng Yang +2
Handling digital images is almost always accompanied by a lossy compression in order to facilitate efficient transmission and storage. This introduces an unavoidable tension betwee…
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
Xiang Zhu, Hossein Talebi, Xinwei Shi +2
In machine learning based single image super-resolution, the degradation model is embedded in training data generation. However, most existing satellite image super-resolution meth…
Better Compression with Deep Pre-Editing
Hossein Talebi, Damien Kelly, Xiyang Luo +4
Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the all…
NIMA: Neural Image Assessment
Hossein Talebi, Peyman Milanfar
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelin…