  This paper proposes a learning-based denoising method called FlashLight CNN
(FLCNN) that implements a deep neural network for image denoising. The proposed
approach is based on deep residual networks and inception networks and it is
able to leverage many more parameters than residual networks alone for
denoising grayscale images corrupted by additive white Gaussian noise (AWGN).
FlashLight CNN demonstrates state of the art performance when compared
quantitatively and visually with the current state of the art image denoising
methods.
