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Snapshot High Dynamic Range Imaging via Sparse Representations and Feature Learning.
- Source :
- IEEE Transactions on Multimedia; Mar2020, Vol. 22 Issue 3, p688-703, 16p
- Publication Year :
- 2020
-
Abstract
- Bracketed High Dynamic Range (HDR) imaging architectures acquire a sequence of Low Dynamic Range (LDR) images in order to either produce a HDR image or an “optimally” exposed LDR image, achieving impressive results under static camera and scene conditions. However, in real world conditions, ghost-like artifacts and noise effects limit the quality of HDR reconstruction. We address these limitations by introducing a post-acquisition snapshot HDR enhancement scheme that generates a bracketed sequence from a small set of LDR images, and in the extreme case, directly from a single exposure. We achieve this goal via a sparse-based approach where transformations between differently exposed images are encoded through a dictionary learning process, while we learn appropriate features by employing a stacked sparse autoencoder (SSAE) based framework. Via experiments with real images, we demonstrate the improved performance of our method over the state-of-the-art, while our single-shot based HDR formulation provides a novel paradigm for the enhancement of LDR imaging and video sequences. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15209210
- Volume :
- 22
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Multimedia
- Publication Type :
- Academic Journal
- Accession number :
- 141900377
- Full Text :
- https://doi.org/10.1109/TMM.2019.2933333