Image Denoising using Dark Frames

In digital images there are multiple sources of noise. Typically, the noise increases on increasing ths ISO but some noise is still observable at lower ISOs as well, especially in underexposed regions of the image. Also, it is often the case that there are patterns in noise specific to the camera being used. While it’s true that on increasing the ISO, random noise dominates; fixed pattern noise is more observable at lower ISOs. While there exists a large body of work that models noise as independent and Guassian at every pixel, it is not completely true, especially at lower ISOs. Camera noise can be observed in isolation by capturing dark frames, i.e., by taking images with shutter closed or lens cap on. A naive method to remove noise is to simply subtract the dark frame from a captured image [2]. One can improve upon it by capturing a number of dark frames, taking their average and then subtracting it from the captured image. However, in this work we aim to model the noise statistics better and couple it with natural image statistics to perform denoising. Image denoising is a hot area of research in image processing community. However, the focus of majority of work has been to model natural image statistics while assuming Gaussian per pixel independent noise [3]. Here, we take a complimentary approach where we primarily focus on learning and modeling noise.