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.
License type:
Funding Info:
This work was supported by the National Nature ScienceFoundation of China under Project 61775172 and Project61620106012.