Deep Multi-Stage Learning for HDR With Large Object Motions

High Dynamic Range (HDR) imaging provides a methodology to capture a wide luminance range in a single image which traditional imaging techniques fail to capture. State of the art deep learning methods in multi-frame HDR imaging follow an end-to-end learning approach and often fail to generate realis...

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Bibliographic Details
Published in2019 IEEE International Conference on Image Processing (ICIP) pp. 4714 - 4718
Main Authors K.S., Green Rosh, Biswas, Anmol, Patel, Mandakinee Singh, Prasad, B H Pawan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Cameras
Deep learning
Dynamic range
Exposure Alignment
HDR
Multi Exposure Fusion
Optical imaging
Optical saturation
Training
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Summary:High Dynamic Range (HDR) imaging provides a methodology to capture a wide luminance range in a single image which traditional imaging techniques fail to capture. State of the art deep learning methods in multi-frame HDR imaging follow an end-to-end learning approach and often fail to generate realistic details in large occluded regions. In this paper, we propose to split the HDR problem into multiple stages and tackle them using separate Convolutional Neural Networks (CNNs) rather than attempting an end-to-end learning. First, in the Exposure Alignment Stage, we propose to generate virtual exposure images which are similar in structure to a chosen reference input using deep CNNs. This is followed by an HDR Merge stage, where another CNN learns to generate the HDR output from the virtual exposure images. We perform extensive comparative studies to show that the proposed method generates artifact-free outputs with plausible details in occluded regions.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803582