Single Image Brightening via Multi-Scale Exposure Fusion with Hybrid Learning
This work addresses image quality enhancement for photography in low-light conditions, representing an incremental improvement over existing brightening algorithms.
The paper tackles the problem of brightening dark images captured in low-light conditions by introducing a hybrid learning framework that combines model-driven intensity mapping with data-driven neural network enhancement, followed by multi-scale exposure fusion. Experimental results show the algorithm outperforms existing methods on the MEF-SSIM metric.
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.