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Found 35 papers, 10 papers with code
The Power of Context: How Multimodality Improves Image Super-Resolution
Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inputs.
On the Relation Between Linear Diffusion and Power Iteration
These models learn the implicit distribution given by the training dataset, and sample new data by transforming random noise through the reverse process, which can be thought of as gradual denoising.
Stochastic Deep Restoration Priors for Imaging Inverse Problems
We introduce Stochastic deep Restoration Priors (ShaRP), a novel method that leverages an ensemble of such restoration models to regularize inverse problems.
A Survey on Diffusion Models for Inverse Problems
Diffusion models have become increasingly popular for generative modeling due to their ability to generate high-quality samples.
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
Denoising, the process of reducing random fluctuations in a signal to emphasize essential patterns, has been a fundamental problem of interest since the dawn of modern scientific inquiry.
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency.
SPIRE: Semantic Prompt-Driven Image Restoration
In this paper, we develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework that leverages natural language as a user-friendly interface to control the image restoration process.
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
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.
A Restoration Network as an Implicit Prior
Image denoisers have been shown to be powerful priors for solving inverse problems in imaging.
Prompt-tuning latent diffusion models for inverse problems
We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors.
Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
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.
Multiscale Structure Guided Diffusion for Image Deblurring
We evaluate a single-dataset trained model on diverse datasets and demonstrate more robust deblurring results with fewer artifacts on unseen data.
Soft Diffusion: Score Matching for General Corruptions
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.
Ranked #8 on
Image Generation
on CelebA 64x64
Deblurring via Stochastic Refinement
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.
Interpretable Unsupervised Diversity Denoising and Artefact Removal
This work presents an interpretable approach for unsupervised and diverse image restoration.
Mobile Computational Photography: A Tour
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
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.
Projected Distribution Loss for Image Enhancement
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.
Polyblur: Removing mild blur by polynomial reblurring
The proposed method estimates and removes mild blur from a 12MP image on a modern mobile phone in a fraction of a second.
Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
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)
Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method
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.
Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior
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
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
Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image.
Efficient Blind Deblurring under High Noise Levels
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.
Image Anomalies: a Review and Synthesis of Detection Methods
We review the broad variety of methods that have been proposed for anomaly detection in images.
Modeling Realistic Degradations in Non-blind Deconvolution
We show that accurately modeling a more realistic image acquisition pipeline leads to significant improvements, both in terms of image quality and PSNR.
Deep Video Deblurring for Hand-Held Cameras
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.
Ranked #5 on
Deblurring
on Beam-Splitter Deblurring (BSD)
Deep Video Deblurring
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.
Fundamental Limits in Multi-image Alignment
In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available.
An analysis of the factors affecting keypoint stability in scale-space
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.
Hand-held Video Deblurring via Efficient Fourier Aggregation
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
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
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
We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors.
