A Nonsmooth Graph-Based Approach to Light Field Super-Resolution

Mireille El Gheche, Pascal Frossard, Mattia Rossi
2018
Article de conférence

In this article we propose a new super-resolution algorithm tailored for light field cameras, which suffer by design from a limited spatial resolution. To do so, we cast the light field super-resolution problem into an optimization problem, where the particular structure of the light field data is captured by a nonsmooth graph-based regularizer, and all the light field views are super-resolved jointly. In our experiments, we show that the proposed method compares favorably to the state-of-the-art light field super-resolution algorithms in terms of PSNR and visual quality. In particular, the nonsmooth graph-based regularizer leads to sharper images while preserving fine details.

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Mireille El Gheche, Pascal Frossard, Mattia Rossi
2018
Article de conférence

Résumé

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https://infoscience.epfl.ch/record/255245?ln=fr

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Concepts associés (9)

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Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two catego

Light field

The light field is a vector function that describes the amount of light flowing in every direction through every point in space. The space of all possible light rays is given by the five-dimensional

Appareil photographique plénoptique

Un appareil photographique plénoptique est un appareil photographique numérique qui utilise une matrice de micro-objectifs captant l'information de profondeur du champ lumineux, composée de l'intensi

Geometry-Consistent Light Field Super-Resolution via Graph-Based Regularization

Pascal Frossard, Mattia Rossi

Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution, which should therefore be augmented by computational methods. On the one hand, off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they do not consider its particular structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to estimate an explicit disparity map at each view. In this work we propose a new light field super-resolution algorithm meant to address these limitations. We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field. We show that coupling the multi-frame approach with a graph regularizer, that enforces the light field structure via nonlocal self similarities, permits to avoid the costly and challenging disparity estimation step for all the views. Extensive experiments show that the new algorithm compares favorably to the other state-of-the-art methods for light field super-resolution, both in terms of PSNR and visual quality.

2018

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Graph-Based Light Field Super-Resolution

Pascal Frossard, Mattia Rossi

Light field cameras can capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post capture refocus, to depth estimation and image-based rendering. However, light field cameras exhibit a very limited spatial resolution, which should therefore be increased by computational methods. Off-the-shelf single-frame and multi-frame super-resolution algorithms are not ideal for light field data, as they ignore its particular structure. A few super-resolution algorithms explicitly devised for light field data exist, but they exhibit significant limitations, such as the need to carry out an explicit disparity estimation step for one or several light field views. In this work we present a new light field super-resolution algorithm meant to address these limitations. We adopt a multi- frame alike super-resolution approach, where the information in the different light field views is used to augment the spatial resolution of the whole light field. In particular, we show that coupling the multi-frame paradigma with a graph regularizer that enforces the light field structure permits to avoid the costly and challenging disparity estimation step. Our experiments show that the proposed method compares favorably to the state-of-the- art for light field super-resolution algorithms, both in terms of PSNR and visual quality.

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Light ﬁeld panorama by a plenoptic camera

Loïc Arnaud Baboulaz, Paolo Prandoni, Martin Vetterli, Zhou Xue

Consumer-grade plenoptic camera Lytro draws a lot of interest from both academic and industrial world. However its low resolution in both spatial and angular domain prevents it from being used for ﬁne and detailed light ﬁeld acquisition. This paper proposes to use a plenoptic camera as an image scanner and perform light ﬁeld stitching to increase the size of the acquired light ﬁeld data. We consider a simpliﬁed plenoptic camera model comprising a pinhole camera moving behind a thin lens. Based on this model, we describe how to perform light ﬁeld acquisition and stitching under two diﬀerent scenarios: by camera translation or by camera translation and rotation. In both cases, we assume the camera motion to be known. In the case of camera translation, we show how the acquired light ﬁelds should be resampled to increase the spatial range and ultimately obtain a wider ﬁeld of view. In the case of camera translation and rotation, the camera motion is calculated such that the light ﬁelds can be directly stitched and extended in the angular domain. Simulation results verify our approach and demonstrate the potential of the motion model for further light ﬁeld applications such as registration and super-resolution.

SPIE2013

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