Flashlight CNN Image Denoising
This addresses the problem of image denoising for applications like photography or medical imaging, but it appears incremental as it builds on existing deep learning methods.
The paper tackles image denoising for grayscale images corrupted by additive white Gaussian noise, proposing FlashLight CNN (FLCNN) based on deep residual and inception networks, which achieves state-of-the-art performance in quantitative and visual comparisons.
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.