  We address the problem of exposure correction of dark, blurry and noisy
images captured in low-light conditions in the wild. Classical image-denoising
filters work well in the frequency space but are constrained by several factors
such as the correct choice of thresholds, frequency estimates etc. On the other
hand, traditional deep networks are trained end-to-end in the RGB space by
formulating this task as an image-translation problem. However, that is done
without any explicit constraints on the inherent noise of the dark images and
thus produce noisy and blurry outputs. To this end we propose a DCT/FFT based
multi-scale loss function, which when combined with traditional losses, trains
a network to translate the important features for visually pleasing output. Our
loss function is end-to-end differentiable, scale-agnostic, and generic; i.e.,
it can be applied to both RAW and JPEG images in most existing frameworks
without additional overhead. Using this loss function, we report significant
improvements over the state-of-the-art using quantitative metrics and
subjective tests.
