Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis

Sharma, R; Perry, S; Cheng, E

Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis

Sharma, R Perry, S

Cheng, E

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors, 22, (5), pp. 1993-1993

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Field	Value	Language
dc.contributor.author	Sharma, R
dc.contributor.author	Perry, S https://orcid.org/0000-0002-2794-3178
dc.contributor.author	Cheng, E
dc.date.accessioned	2022-03-09T21:08:49Z
dc.date.available	2022-03-09T21:08:49Z
dc.identifier.citation	Sensors, 22, (5), pp. 1993-1993
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/155118
dc.description.abstract	<jats:p>Depth estimation for light field images is essential for applications such as light field image compression, reconstructing perspective views and 3D reconstruction. Previous depth map estimation approaches do not capture sharp transitions around object boundaries due to occlusions, making many of the current approaches unreliable at depth discontinuities. This is especially the case for light field images because the pixels do not exhibit photo-consistency in the presence of occlusions. In this paper, we propose an algorithm to estimate the depth map for light field images using depth from defocus. Our approach uses a small patch size of pixels in each focal stack image for comparing defocus cues, allowing the algorithm to generate sharper depth boundaries. Then, in contrast to existing approaches that use defocus cues for depth estimation, we use frequency domain analysis image similarity checking to generate the depth map. Processing in the frequency domain reduces the individual pixel errors that occur while directly comparing RGB images, making the algorithm more resilient to noise. The algorithm has been evaluated on both a synthetic image dataset and real-world images in the JPEG dataset. Experimental results demonstrate that our proposed algorithm outperforms state-of-the-art depth estimation techniques for light field images, particularly in case of noisy images.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors
dc.relation.isbasedon	10.3390/s22051993
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Noise-Resilient Depth Estimation for Light Field Images Using Focal Stack and FFT Analysis
dc.type	Journal Article
utslib.citation.volume	22
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-09T21:08:45Z
pubs.issue	5
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	5

Abstract:

Depth estimation for light field images is essential for applications such as light field image compression, reconstructing perspective views and 3D reconstruction. Previous depth map estimation approaches do not capture sharp transitions around object boundaries due to occlusions, making many of the current approaches unreliable at depth discontinuities. This is especially the case for light field images because the pixels do not exhibit photo-consistency in the presence of occlusions. In this paper, we propose an algorithm to estimate the depth map for light field images using depth from defocus. Our approach uses a small patch size of pixels in each focal stack image for comparing defocus cues, allowing the algorithm to generate sharper depth boundaries. Then, in contrast to existing approaches that use defocus cues for depth estimation, we use frequency domain analysis image similarity checking to generate the depth map. Processing in the frequency domain reduces the individual pixel errors that occur while directly comparing RGB images, making the algorithm more resilient to noise. The algorithm has been evaluated on both a synthetic image dataset and real-world images in the JPEG dataset. Experimental results demonstrate that our proposed algorithm outperforms state-of-the-art depth estimation techniques for light field images, particularly in case of noisy images.

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http://hdl.handle.net/10453/155118