Prabhakar, KR and Agrawal, S and Singh, DK and Ashwath, B and Babu, RV (2020) Towards Practical and Efficient High-Resolution HDR Deghosting with CNN. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 23 - 28 August 2020, Glasgow, pp. 497-513.
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Abstract
Generating High Dynamic Range (HDR) image in the presence of camera and object motion is a tedious task. If uncorrected, these motions will manifest as ghosting artifacts in the fused HDR image. On one end of the spectrum, there exist methods that generate high-quality results that are computationally demanding and too slow. On the other end, there are few faster methods that produce unsatisfactory results. With ever increasing sensor/display resolution, currently we are very much in need of faster methods that produce high-quality images. In this paper, we present a deep neural network based approach to generate high-quality ghost-free HDR for high-resolution images. Our proposed method is fast and fuses a sequence of three high-resolution images (16-megapixel resolution) in about 10 s. Through experiments and ablations, on different publicly available datasets, we show that the proposed method achieves state-of-the-art performance in terms of accuracy and speed. © 2020, Springer Nature Switzerland AG.
Item Type: | Conference Paper |
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Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Publisher: | Springer Science and Business Media Deutschland GmbH |
Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH. |
Keywords: | Deep neural networks, Ghosting artifacts; High dynamic range images; High quality images; High resolution; High resolution image; Network-based approach; Object motion; State-of-the-art performance, Computer vision |
Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
Date Deposited: | 07 Feb 2023 09:18 |
Last Modified: | 07 Feb 2023 09:18 |
URI: | https://eprints.iisc.ac.in/id/eprint/79994 |
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