Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning

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Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
Title:
Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
Other Titles:
IEEE Transactions on Circuits and Systems for Video Technology
Publication Date:
01 July 2020
Citation:
Abstract:
A small ISO and a small exposure time are usually used to capture an image in back- or low-lighting condition which results in an image with negligible motion blur and small noise but looks dark. In this paper, a single image brightening algorithm is introduced to brighten such an image. The proposed algorithm includes a unique hybrid learning framework to generate two virtual images with large exposure times. The virtual images are first generated via intensity mapping functions (IMFs) which are computed using camera response functions (CRFs) and this is a model-driven approach. Both the virtual images are then enhanced by using a data-driven approach, i.e. a residual convolutional neural network to approach the ground truth images. The model-driven approach and the datadriven one compensate each other in the proposed hybrid learning framework. The final brightened image is obtained by fusing the original image and two virtual images via a multi-scale exposure fusion algorithm with properly defined weights. Experimental results show that the proposed brightening algorithm outperforms existing algorithms in terms of MEF-SSIM metric.
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Funding Info:
This work was supported by the National Nature ScienceFoundation of China under Project 61775172 and Project61620106012.
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