Depth map estimation using census transform for light field cameras

Tomioka, Takayuki; Mishiba, Kazu; Oyamada, Yuji; Kondo, Katsuya

doi:10.1109/icassp.2016.7471955

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depth estimation for the lense-array type cameras is a challenging problem [2]- [11]. Unlike the camera-array type cameras, baseline between a sub-aperture image pair is extremely short.…”

Section: Introductionmentioning

confidence: 99%

Depth Map Estimation Using Census Transform for Light Field Cameras

Tomioka

Mishiba

Oyamada

et al. 2017

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYDepth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.

show abstract

“…Depth estimation for the lense-array type cameras is a challenging problem [2]- [11]. Unlike the camera-array type cameras, baseline between a sub-aperture image pair is extremely short.…”

Section: Introductionmentioning

confidence: 99%