  A small ISO and a small exposure time are usually used to capture an image in
the back or low light conditions which results in an image with negligible
motion blur and small noise but look 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 data-driven 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
the MEF-SSIM metric.
