Search Results for author: Mauricio Delbracio

Found 30 papers, 10 papers with code

Bigger is not Always Better: Scaling Properties of Latent Diffusion Models

no code implementations1 Apr 2024 Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar

We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.

TIP: Text-Driven Image Processing with Semantic and Restoration Instructions

no code implementations18 Dec 2023 Chenyang Qi, Zhengzhong Tu, Keren Ye, Mauricio Delbracio, Peyman Milanfar, Qifeng Chen, Hossein Talebi

Text-driven diffusion models have become increasingly popular for various image editing tasks, including inpainting, stylization, and object replacement.

Deblurring Denoising +2

CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation

1 code implementation2 Oct 2023 Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M. Patel, Peyman Milanfar

Our conditional-task learning and distillation approach outperforms previous distillation methods, achieving a new state-of-the-art in producing high-quality images with very few steps (e. g., 1-4) across multiple tasks, including super-resolution, text-guided image editing, and depth-to-image generation.

Image Enhancement Super-Resolution +1

A Restoration Network as an Implicit Prior

no code implementations2 Oct 2023 Yuyang Hu, Mauricio Delbracio, Peyman Milanfar, Ulugbek S. Kamilov

Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.

Image Restoration Super-Resolution

Prompt-tuning latent diffusion models for inverse problems

no code implementations2 Oct 2023 Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio

We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors.

Deblurring Super-Resolution

Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration

1 code implementation20 Mar 2023 Mauricio Delbracio, Peyman Milanfar

In conditional denoising diffusion image restoration the denoising network generates the restored image by repeatedly denoising an initial image of pure noise, conditioned on the degraded input.

Deblurring Denoising +3

Multiscale Structure Guided Diffusion for Image Deblurring

no code implementations ICCV 2023 Mengwei Ren, Mauricio Delbracio, Hossein Talebi, Guido Gerig, Peyman Milanfar

We evaluate a single-dataset trained model on diverse datasets and demonstrate more robust deblurring results with fewer artifacts on unseen data.

Deblurring Denoising +2

Soft Diffusion: Score Matching for General Corruptions

no code implementations12 Sep 2022 Giannis Daras, Mauricio Delbracio, Hossein Talebi, Alexandros G. Dimakis, Peyman Milanfar

To reverse these general diffusions, we propose a new objective called Soft Score Matching that provably learns the score function for any linear corruption process and yields state of the art results for CelebA.

Denoising Image Generation

Deblurring via Stochastic Refinement

no code implementations CVPR 2022 Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, Peyman Milanfar

Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input.

Deblurring Image Deblurring

Mobile Computational Photography: A Tour

no code implementations17 Feb 2021 Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar

The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of.


Non-uniform Blur Kernel Estimation via Adaptive Basis Decomposition

1 code implementation1 Feb 2021 Guillermo Carbajal, Patricia Vitoria, Mauricio Delbracio, Pablo Musé, José Lezama

In recent years, the removal of motion blur in photographs has seen impressive progress in the hands of deep learning-based methods, trained to map directly from blurry to sharp images.

Deblurring Image Restoration

Polyblur: Removing mild blur by polynomial reblurring

no code implementations16 Dec 2020 Mauricio Delbracio, Ignacio Garcia-Dorado, Sungjoon Choi, Damien Kelly, Peyman Milanfar

The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.

Deblurring Super-Resolution

Projected Distribution Loss for Image Enhancement

2 code implementations16 Dec 2020 Mauricio Delbracio, Hossein Talebi, Peyman Milanfar

More explicitly, we show that in imaging applications such as denoising, super-resolution, demosaicing, deblurring and JPEG artifact removal, the proposed learning loss outperforms the current state-of-the-art on reference-based perceptual losses.

Deblurring Demosaicking +6

Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data

1 code implementation ICCV 2021 Abdullah Abuolaim, Mauricio Delbracio, Damien Kelly, Michael S. Brown, Peyman Milanfar

Leveraging these realistic synthetic DP images, we introduce a recurrent convolutional network (RCN) architecture that improves deblurring results and is suitable for use with single-frame and multi-frame data (e. g., video) captured by DP sensors.

Ranked #13 on Image Defocus Deblurring on DPD (Dual-view) (using extra training data)

Deblurring Image Defocus Deblurring

Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method

no code implementations3 Apr 2020 J. Matias Di Martino, Fernando Suzacq, Mauricio Delbracio, Qiang Qiu, Guillermo Sapiro

Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e. g., to spoofing attacks and low-light conditions.

3D Reconstruction Face Recognition

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior

1 code implementation14 Nov 2019 Mario González, Andrés Almansa, Mauricio Delbracio, Pablo Musé, Pauline Tan

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model.

Detecting Out-Of-Distribution Samples Using Low-Order Deep Features Statistics

no code implementations ICLR 2019 Igor M. Quintanilha, Roberto de M. E. Filho, José Lezama, Mauricio Delbracio, Leonardo O. Nunes

The ability to detect when an input sample was not drawn from the training distribution is an important desirable property of deep neural networks.


Reducing Anomaly Detection in Images to Detection in Noise

no code implementations25 Apr 2019 Axel Davy, Thibaud Ehret, Jean-Michel Morel, Mauricio Delbracio

Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.

Anomaly Detection

Efficient Blind Deblurring under High Noise Levels

1 code implementation19 Apr 2019 Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo

In this work, we first show that current state-of-the-art kernel estimation methods based on the $\ell_0$ gradient prior can be adapted to handle high noise levels while keeping their efficiency.

Blind Image Deblurring Denoising +2

Image Anomalies: a Review and Synthesis of Detection Methods

no code implementations7 Aug 2018 Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio

We review the broad variety of methods that have been proposed for anomaly detection in images.

Anomaly Detection

Modeling Realistic Degradations in Non-blind Deconvolution

no code implementations4 Jun 2018 Jérémy Anger, Mauricio Delbracio, Gabriele Facciolo

We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.

Deblurring Image Deblurring +2

Deep Video Deblurring for Hand-Held Cameras

1 code implementation CVPR 2017 Shuochen Su, Mauricio Delbracio, Jue Wang, Guillermo Sapiro, Wolfgang Heidrich, Oliver Wang

We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

Deblurring Image Deblurring +1

Deep Video Deblurring

1 code implementation25 Nov 2016 Shuochen Su, Mauricio Delbracio, Jue Wang, Guillermo Sapiro, Wolfgang Heidrich, Oliver Wang

We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

Deblurring Image Deblurring +1

Fundamental Limits in Multi-image Alignment

no code implementations4 Feb 2016 Cecilia Aguerrebere, Mauricio Delbracio, Alberto Bartesaghi, Guillermo Sapiro

In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available.

Image Registration

An analysis of the factors affecting keypoint stability in scale-space

no code implementations26 Nov 2015 Ives Rey-Otero, Jean-Michel Morel, Mauricio Delbracio

In practice, however, scale invariance may be weakened by various sources of error inherent to the SIFT implementation affecting the stability and accuracy of keypoint detection.

Keypoint Detection

Hand-held Video Deblurring via Efficient Fourier Aggregation

no code implementations17 Sep 2015 Mauricio Delbracio, Guillermo Sapiro

In this work, we present an algorithm that removes blur due to camera shake by combining information in the Fourier domain from nearby frames in a video.


Burst Deblurring: Removing Camera Shake Through Fourier Burst Accumulation

no code implementations CVPR 2015 Mauricio Delbracio, Guillermo Sapiro

Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem.


Removing Camera Shake via Weighted Fourier Burst Accumulation

no code implementations11 May 2015 Mauricio Delbracio, Guillermo Sapiro

The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude.

Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it

no code implementations8 Sep 2014 Ives Rey-Otero, Mauricio Delbracio, Jean-Michel Morel

We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors.

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