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GHOST-FREE HDR IMAGING VIA UNROLLING LOW-RANK MATRIX COMPLETION

Citation Author(s):
Truong Thanh Nhat Mai, Edmund Y. Lam, Chul Lee
Submitted by:
Truong Mai
Last updated:
23 September 2021 - 10:52pm
Document Type:
Poster
Document Year:
2021
Event:
Presenters Name:
Truong Thanh Nhat Mai

Abstract 

Abstract: 

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm by unrolling low-rank matrix completion. By exploiting the low-rank structure of the irradiance maps from low dynamic range (LDR) images, we formulate ghost-free HDR imaging as a general low-rank matrix completion problem. Then, we solve the problem iteratively using the augmented Lagrange multiplier (ALM) method. At each iteration, the optimization variables are updated by closed-form solutions and the regularizers are updated by learned deep neural networks. Experimental results show that the proposed algorithm provides better image qualities with fewer visual artifacts compared to state-of-the-art algorithms.

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Dataset Files

ICIP 2021 - Conference Poster

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