Low-Light Image Enhancement via a Deep Hybrid Network | IEEE Journals & Magazine | IEEE Xplore

Low-Light Image Enhancement via a Deep Hybrid Network


Abstract:

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of...Show More

Abstract:

Camera sensors often fail to capture clear images or videos in a poorly lit environment. In this paper, we propose a trainable hybrid network to enhance the visibility of such degraded images. The proposed network consists of two distinct streams to simultaneously learn the global content and the salient structures of the clear image in a unified network. More specifically, the content stream estimates the global content of the low-light input through an encoder–decoder network. However, the encoder in the content stream tends to lose some structure details. To remedy this, we propose a novel spatially variant recurrent neural network (RNN) as an edge stream to model edge details, with the guidance of another auto-encoder. The experimental results show that the proposed network favorably performs against the state-of-the-art low-light image enhancement algorithms.
Published in: IEEE Transactions on Image Processing ( Volume: 28, Issue: 9, September 2019)
Page(s): 4364 - 4375
Date of Publication: 16 April 2019

ISSN Information:

PubMed ID: 30998467

Funding Agency:

Citations are not available for this document.

Cites in Papers - |

Cites in Papers - IEEE (165)

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