Publications by Majed El Helou

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Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

Localization of a set of nodes is an important and a thoroughly researched problem in robotics and sensor networks. This paper is concerned with the theory of localization from inner-angle measurements. We focus on the challenging case where no anchor loca ...

Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others. With current image sizes in the order of megabytes, it is extremely exp ...

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one ...

Despite the advances in single-image super resolution using deep convolutional networks, the main problem remains unsolved: recovering fine texture details. Recent works in super resolution aim at modifying the training of neural networks to enable the rec ...

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly. Their priors are hand-designed and their corresponding weights are heuristically assigned. Hence, deep learning methods often produce superior image restoration ...

The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting i ...

Knowledge of lens specifications is important to identify the best lens for a given capture scenario and application. Lens manufacturers provide many specifications in their data sheets, and multiple initiatives for testing and comparing different lenses c ...

Scattering of light due to the presence of aerosol particles along the path of radiation causes atmospheric haze in images. This scattering is significantly less severe in longer wavelength bands than in shorter ones, thus the importance of near-infrared ( ...

Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for sp ...

Depth-from-defocus techniques suffer an ambiguity problem where depth planes on opposite sides of the focal plane have identical defocus. We solve the ambiguity by relying on the wavelength-dependent relationship between defocus and depth. We conduct a rob ...

A computer-implemented video completion method is proposed for reconstructing a sparsely sampled video comprising sparsely sampled video frames comprising sparse sets of picture element intensity values. The method comprises: distributing the sparse sets o ...

Extreme image or video completion, where, for instance, we only retain 1% of pixels in random locations, allows for very cheap sampling in terms of the required pre-processing. The consequence is, however, a reconstruction that is challenging for humans an ...

We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The f ...

Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and ge ...

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-gr ...

This paper reviews the first challenge on spectral image reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3- channel RGB image. The challenge was divided into 2 tracks: the “Clean” track sought HS reco ...

Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multip ...

Majed El Helou

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Deep Image Restoration: Between Data Fidelity and Learned Priors

Realizability of planar point embeddings from angle measurements

Fourier-Domain Optimization for Image Processing

W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

A comparative study on wavelets and residuals in deep super resolution

PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks

AAM: An Assessment Metric of Axial Chromatic Aberration

Near-Infrared Fusion for Photorealistic Image Dehazing

Deep Gaussian denoiser epistemic uncertainty and decoupled dual-attention fusion

Closed-Form Solution to Disambiguate Defocus Blur in Single-Perspective Images

Adaptive extreme video completion

Divergence-Based Adaptive Extreme Video Completion

AIM 2020: Scene Relighting and Illumination Estimation Challenge

Image Denoising with Control over Deep Network Hallucination

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

NTIRE 2018 Challenge on Spectral Reconstruction from RGB Images

NTIRE 2021 Depth Guided Image Relighting Challenge