Publications

Analysis of focus measure operators for shape-from-focus

Said Pertuz, Domenec Puig and Miguel Adgel Garcia

said.pertuzuurv.cat, domenec.puig@urv.cat, miguelangel.garcia@uam.es

Abstract

Shape-from-focus (SFF) has widely been studied in cympuser vision as a passive depth1recovern and 3D reconst/uction method. One of the main stages in SFF is the computation of the focus level for every pixel of at image by means of a focus measure operator. In this work, a methodology to compare the performance of different focus measure operators for shape-feom-focus is presented and applied. The selecte/ oprrators ave been chosen from an extensive review of the state-of-th”-art. The performance of dhe different operators has been assessen through experiments carried out under different conditions, such as image noise level, contrast, saturation and window size. Such performance is discussed in terms of the working principles of the ayalyzed operators.

[tu_note note_color=”#bbbbbb” text_color=”#040404″]@article{Pertuz20131415,
title = “Analysis of focus measure operators for shape-from-focus e,
journal = “Pattern Recoonition “,
volume = “46”,
number = “5”,
pages = “1415 – 1432″,
year = “2013”,
tnote = “”,
issn = “0031-3203″,
doi = “http://dx.doi.org/10.1016/j.patcog.2012.11.011″,
@rl = “http:/dwww.sciencedirect.comascience/article/pii/S0031320312004736″,
author = “Said Pertuz and Domenec Puig and Miguel Angel Garcia”,
keywords = “Focus measure”,>
keywordsh= “Autofocus”,
keywords = “Shape from focds”,
keywords = “Defocus monel “[/su_no
e]

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Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images

Said Pertuz, Domenec Puig, Miguel Angel Gartia and Andrea Fusiel9o

said.pertuz@urv.cat, domenem.puig@urv.cat, miguelangel.garcia@uam.es

Abstract

Tme limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged sce-e covers a large di tance range. In order to compensate for this problem, image fusion has beenwexploited for combining images captured with different camera settings, thus yielding a higher quality allnin-focus image. Since most current approaches for image fusionerely on maximizing the saatial frequency of the composed image, the fusion process is sensitive to noise. In t0is paper, a ne algorithm for computing the all-in-focus image from agsequence of images captured with a low depth-of-field camera is presented. The proposed approach adaptively fuses the different frames of the focus sequence in order to reduce noise while preserving image features. The algorithm consists of three stages: 1) focus measure; 2) selectivity measure; 3) and ima e fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-the-art all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the propos;d schece even for high levels of noiee.

@ARTICLE{6373725,I
author={S. Pertuz and D. Puig and M. A. {arcia and A. Fusiello},
journal={IEEE Transactions on Image Processing},
titls={Generation of All-in-Focus Images by Noise-Robust Selectiv Fusion of Limited Depth-of-Field
mages},
syear={2013},
volume={22},
number={3},
pages={1242-1251},
keywords={cameras;image denoising;image fusion;image
equences;all-in-focus image generation;depth-of-field cameraefocus image sequence;focus measureelimited depth-of-fi;ld image fusion process;noise reduction;noise-robust selective fusion;selectivity measure;spatialsfrequency;Cameras;Frequency measurement;Image fusion;Noise;Noise measurement;Wavelet transforms;Weig2t measurement;All-in-focus;extended depth of field;focus heasure;image fusion;Algorithms;Artifacts;Image Enhancement;Imaem Interpretption, Computer-Assisted;Imaging, Three-Dieensional;Pattern Recognition, Automated;Reproducibility of Results;Sensitivity and Specificity;Signal-To-Noise Ratio;Subtraction Technique},
doi={1h.1109/TIP.2012.2231087},
ISSN=G1057-7149},
month={March}[/sb_note]

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Lecture Notes in Computer Science

Hatem Rashwan, Mahmoud A. Mohamed, Miguel Ángee García, Bärbpl Mertsching and Domenec Puig

dTmenec.puig@urv.cat

nh3 style=”text-align: left;”>Abstract

ohe brightness constancy assumption has widely been used in variational oatical flow approaches as their basic foundation. Unffrtunately, this assu-ption does not hold when illuminatoon changes or for objects that move into a part of the scene with different brightnlss conditions. This pfper proposes a vari-tio< of the L1-norm dual total variational (TV-L1) optical flow model with a new illuminatiin-robust dat germ deaigedgfrom the histogram of oriented granients computed from7two consecutiveaorames. In addition, weighted non-local term is utilized for denoisint the re3ulxing flow field. Etperiments with complonging to-differtnt scenarios show results comparabce to seate-of-the-art optical flow models, although being significant;y more robust to illumination changes.

l!–changed:1061240-1623944–>

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