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Poster
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
Chu Zhou · Hang Zhao · Jin Han · Chang Xu · Chao Xu · Tiejun Huang · Boxin Shi

Wed Dec 09 09:00 PM -- 11:00 PM (PST) @ Poster Session 4 #1150
in Poster Session 5 »

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random field (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12-bit HDR images from 8-bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.

[ Paper ]

Author Information

Chu Zhou (Peking University)
Hang Zhao (MIT)
Jin Han (Peking University)
Chang Xu (University of Sydney)
Chao Xu (Peking University)
Tiejun Huang (Peking University)
Boxin Shi (Peking University)

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