Low Light Image Enhancement via Sparse Representations

Enhancing the quality of low light images is a critical processing function both from an aesthetics and an information extraction point of view. This work proposes a novel approach for enhancing images captured under low illumination conditions based on the mathematical framework of Sparse Representations. In our model, we utilize the sparse representation of low light image patches in an appropriate dictionary to approximate the corresponding day-time images. We consider two dictionaries; a night dictionary for low light conditions and a day dictionary for well illuminated conditions. To approximate the generation of low and high illumination image pairs, we generated the day dictionary from patches taken from well exposed images, while the night dictionary is created by extracting appropriate features from under exposed image patches. Experimental results suggest that the proposed scheme is able to accurately estimate a well illuminated image given a low-illumination version. The effectiveness of our system is evaluated by comparisons against ground truth images while compared to other methods for image night context enhancement, our system achieves better results both quantitatively as well as qualitatively.