论文标题
手电筒CNN图像Denoising
Flashlight CNN Image Denoising
论文作者
论文摘要
本文提出了一种基于学习的DeNoising方法，称为手电筒CNN（FLCNN），该方法实现了深层神经网络以进行图像denoising。所提出的方法基于深层残留网络和启动网络，它比单独的残留网络能够利用更多的参数来降低被添加性白色高斯噪声（AWGN）损坏的灰度图像。手电筒CNN在定量和视觉上与当前图像剥落方法的当前状态进行比较时，表现出最先进的性能。
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.
