Fast Bright-Pass Bilateral Filtering for Low-Light Enhancement

We consider the problem of enhancing images captured under low-light conditions. Several variational and filtering based solutions have been proposed for this problem that are based on the retinex model. The idea in retinex is to first estimate the illumination and reflectance from the observed image, enhance the illumination, and then combine it with the reflectance to get the rectified image. A variant of bilateral filtering, called bright-pass bilateral filtering (BPBF), can be used for illumination estimation. However, BPBF is computation intensive and takes up a significant amount of the processing time. Motivated by recent work, we propose a Fourier approximation of BPBF that can accelerate the filtering (by an order) without loss in visual quality. Experimental results demonstrate that our algorithm is sufficiently fast and can effectively enhance low-light images. In particular, our proposal is competitive with recent algorithms in terms of visual perception and quality metrics.