Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Malik, Aamir Saeed; Choi, Tae‐Sun

doi:10.1016/j.patcog.2006.05.032

Cited by 117 publications

(61 citation statements)

References 7 publications

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“…Given this local behaviour, a certain depth regularity of the observed surface is implicitly assumed. On the one hand, the considered neighborhood has to be as small as possible to guarantee an approximately constant depth within itself and therefore to avoid too much "smoothing" the restoration process around sharp depth slopes and even depth discontinuities (Malik & Choi, 2007). On the other hand, it has to be as large as possible both to always capture focus cues (i.e.…”

Section: Neighborhood-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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Section: Neighborhood-based Methodsmentioning

confidence: 99%

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Fernandes¹,

Gavet²,

Pinoli³

2012

Image Restoration - Recent Advances and Applications

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“…A formula used for the suggested operators are given in (5) and (6). The operators are used to find the optimal focused window by measuring the highest response resulted by the operator.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Many FM operators have been proposed in the literature for both autofocus (AF) and SFF applications [5]. In [6], the performance of multiple FM operators were examined. FM operators can be classified into two broad types: space domain and, frequency domain [7] [8], and others such as compression operators [5] [9].…”

Section: Introductionmentioning

confidence: 99%

All in Focus Image Generation based on New Focusing Measure Operators

Shamardan¹

2016

ijacsa

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“…The selection of r is a trade-off between robustness to noise and spatial resolution [16] and is, hence, application-dependent. A radius r = 9 has experimentally been defined in this work.…”

Section: A Focus Measurementioning

confidence: 99%

Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images

Pertuz

Puig

García

et al. 2013

IEEE Trans. on Image Process.

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Abstract-The limited depth-of-field of some cameras prevents them from capturing perfectly focused images when the imaged scene covers a large distance range. In order to compensate for this problem, image fusion has been exploited for combining images captured with different camera settings, thus yielding a higher quality all-in-focus image. Since most current approaches for image fusion rely on maximizing the spatial frequency of the composed image, the fusion process is sensitive to noise. In this work, a new algorithm for computing the all-in-focus image from a sequence 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: focus measure, selectivity measure and image fusion. An extensive set of experimental tests has been carried out in order to compare the proposed algorithm with state-of-theart all-in-focus methods using both synthetic and real sequences. The obtained results show the advantages of the proposed scheme even for high levels of noise.Index Terms-All-in-focus, image fusion, extended depth of field, focus measure

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Consideration of illumination effects and optimization of window size for accurate calculation of depth map for 3D shape recovery

Cited by 117 publications

References 7 publications

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

Surface Topography and Texture Restoration from Sectional Optical Imaging by Focus Analysis

All in Focus Image Generation based on New Focusing Measure Operators

Generation of All-in-Focus Images by Noise-Robust Selective Fusion of Limited Depth-of-Field Images

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