IPOLIPOLhttp://www.ipol.im/feed/IPOL Preprints — Latest public preprints from IPOL.ikiwiki2018-10-04T22:01:17ZInterpolation of Missing Samples in Sound Signals Based on Autoregressive Modelinghttp://www.ipol.im/pub/pre/23/Laurent Oudre2018-10-04T22:01:17Z2018-10-04T22:01:17Z
This article proposes an implementation and a study of the paper Adaptive Interpolation of Discrete-Time Signals That Can Be Modeled as Autoregressive Processes by Janssen et al. The algorithm presented in this paper allows one to reconstruct an audio signal which presents localized degradations by interpolating the missing samples. This method assumes that the signal can locally be modeled as a realization of an autoregressive process and iteratively estimate the model parameters and the interpolated samples by minimizing a quadratic criterion. We investigate the limits and the algorithmic aspects of this method on several audio examples.
An Implementation of the Exposure Fusion Algorithmhttp://www.ipol.im/pub/pre/230/Charles Hessel2018-09-25T12:07:00Z2018-09-13T22:37:17Z
Exposure Fusion is a high dynamic range imaging technique to fuse a bracketed exposure
sequence into a high quality image, introduced in 2009 by Mertens et al.. Contrarily to most HDR
imaging method, exposure fusion does not construct an intermediate HDR image but directly
constructs the final LDR one by seamlessly fusing the best regions of the input sequence, using
the Laplacian pyramid. Since its publication, this method received considerable attention, being
both effective and efficient. We propose in this paper a precise description of the method and
an analysis of its main limitation, an out-of-range artifact.
An Analysis and Implementation of the FFDNet Image Denoising Methodhttp://www.ipol.im/pub/pre/231/Matias Tassano,
Julie Delon,
Thomas Veit2018-09-13T23:34:24Z2018-08-17T15:40:09Z
FFDNet is a recent image denoising method based on a convolutional neural network
architecture. In contrast to other existing neural network denoisers, FFDNet exhibits several
desirable properties such as faster execution time and smaller memory footprint, and the ability
to handle a wide range of noise levels effectively with a single network model. The combination
between its denoising performance and lower computational load makes this algorithm attractive
for practical denoising applications. In this paper we propose an open-source implementation
of the method based on PyTorch, a popular machine learning library for Python. Code for the
training of the network is also provided. We also discuss the characteristics of the architecture
of this algorithm and we compare it to other similar methods.
Implementation of a Denoising Algorithm based on High-Order Singular Value Decomposition of Tensorshttp://www.ipol.im/pub/pre/226/Fabien Feschet2018-09-13T23:34:24Z2018-05-30T09:57:10Z
This article presents an implementation of a denoising algorithm based on High-Order Singular
Value Decomposition (HOSVD) of tensors. It belongs to the class of patch-based methods such
as BM3D and NL-Bayes. It exploits the grouping of similar patches in a local neighbourhood
into a 3D matrix also called a third order tensor. Instead of performing different processing
in different dimension, as in BM3D for instance, it is based on the decomposition of a tensor
simultaneously in all dimensions reducing it to a core tensor in a similar way as SVD does for
matrices in computing the diagonal matrix of singular values. The core tensor is filtered and
a tensor is reconstructed by inverting the HOSVD. As common in patch-based algorithms, all
tensors containing a pixel are then merged to produce an output image.
Improvements of the Inverse Compositional Algorithm for Parametric Motion Estimationhttp://www.ipol.im/pub/pre/222/Thibaud Briand,
Gabriele Facciolo,
Javier Sánchez2018-09-13T23:34:24Z2018-04-19T08:34:16Z
In this work, we propose several improvements of the inverse compositional algorithm for parametric registration. We propose an improved handling of boundary pixels, a different color handling and gradient estimation, and the possibility to skip scales in the multiscale coarse-to-fine scheme. In an experimental part, we analyze the influence of the modifications. The estimation accuracy is at least improved by a factor 1.3 while the computation time is at least reduced by a factor 2.2 for color images.
Comparison of Optical Flow Methods under Stereomatching with Short Baselineshttp://www.ipol.im/pub/pre/217/Tristan Dagobert,
Nelson Monzón,
Javier Sánchez2018-07-22T11:40:36Z2017-10-16T12:24:59Z
This article studies the effectiveness of optical flow methods employed in the case of short baselines and different noise levels. New metrics have been developed to analyze the evaluation results because the usual metrics are inadequate in a subpixel context. Experiments conducted on the adequate Middlebury and CMLA dataset pairs show that the Brox et al. method produces the best errors, with a 60% success rate in relative precision at 1/100 th of a pixel. On the other hand, our comparison shows that the Monzón et al. method also provides competitive results at the same time that it yields disparities with more details and correct contours.
An Affine Invariant Patch Similarityhttp://www.ipol.im/pub/pre/202/Vadim Fedorov,
Coloma Ballester2018-07-22T11:40:36Z2017-07-14T10:09:29Z
Image and video comparison is often approached by comparing patches of visual information. In this work we present a detailed description and implementation of an affine invariant patch similarity measure that performs an appropriate patch comparison by automatically and intrinsically adapting the size and shape of the patches. We also describe the complete implementation of the proposed iterative algorithm for computation of those shape-adaptive patches around each point in the image domain.
Structural Similarity Metrics for Quality Image Fusion
Assessment: Algorithmshttp://www.ipol.im/pub/pre/196/Silvina Pistonesi,
Jorge Martinez,
Silvia Maria Ojeda,
Ronny Vallejos2018-07-22T11:40:36Z2017-04-13T22:43:08Z
The wide use of image fusion techniques in different fields such as medical diagnostics, digital
camera vision, military and surveillance applications, among others, has motivated the
development of various image quality fusion metrics, in order to evaluate them. In this paper, we
study and implement the algorithms of non-reference image structural similarity based metrics
for fusion assessment: Piella’s metric, Cvejic’s metric, Yang’s metric, and Codispersion Fusion
Quality metric. We conduct the comparative experiment of the selected image fusion metrics
over four multiresolution image fusion algorithms, performed on different pairs of images used
in different applications.
Joint Large-Scale Motion Estimation and Image Reconstructionhttp://www.ipol.im/pub/pre/193/Hendrik Dirks2018-07-22T11:40:36Z2016-11-24T13:13:55Z
This article describes the implementation of the joint motion estimation and image reconstruction framework presented by Burger, Dirks and Schönlieb and extends this framework to large-scale motion between consecutive image frames. The variational framework uses displacements between consecutive frames based on the optical
flow approach to improve the image reconstruction quality on the one hand and the motion estimation quality on the other. The energy functional consists of a
data-fidelity term with a general operator that connects the input sequence to the solution, it has a total variation term for the image sequence and is connected to the underlying flow using an optical flow term. Additional spatial regularity for the flow is modeled by a total variation regularizer for both components of the flow. The numerical minimization is performed in an alternating manner using
primal-dual techniques. The resulting schemes are presented as pseudo-code together with a short numerical evaluation.
Joint TV-L1 Optical Flow and Occlusion Estimationhttp://www.ipol.im/pub/pre/118/Juan Francisco Garamendi Bragado,
Coloma Ballester,
Lluís Garrido,
Vanel Lazcano,
Vicent Caselles2018-07-22T11:40:36Z2015-02-05T12:37:40Z
This document describes an implementation of the energy functional minimization proposed by Ballester, Garrido, Lazcano and Caselles for joint optical flow and occlusion estimation. The method is based on the TV-L1 approach introduced Zach, Pock and Bischof in 2007 but with the particularity of detecting occlusions. The energy functional is composed by a regularization term (over the optical flow and the occlusion fields) using the total variation, a data term using the L1 norm, and a term, which is based on the divergence of the flow, for dealing with the occlusions.
Study of the Principal Component Analysis Method for the Correction of Images Degraded by Turbulencehttp://www.ipol.im/pub/pre/47/Tristan Dagobert,
Yohann Tendero,
Stéphane Landeau2018-07-22T11:40:36Z2013-06-29T03:13:25Z
This article details the use of principal component analysis
in order to restore images degraded by atmospheric turbulence. It
analyzes and discusses a well-known paper and proposes a
generalization of the algorithm described in such article.
Examples using sequences of real atmospheric turbulence are
presented.
Real atmospheric turbulent image acquisition is described and
sequences are made accessible for downloading.