基于真实场景图像下的卷积神经网络去噪算法

摘要

为了有效地去除实际图像中的噪声,提出了一种基于真实场景图像下卷积神经网络去噪算法,通过构建新的无噪图像数据集,输入至卷积神经网络中进行训练,并结合模拟退火算法提高训练率,建立去噪网络模型,实现真实场景图像去噪.实验结果表明:含噪的灰度图像与相机拍摄图像均取得明显的平滑效果,算法信号-噪音功率比(PSNR)值较高,图像边缘和细节也得到了较好的保留.%An denoising algorithm of convolutional neural network (CNN ) based on actual scene image is proposed. By building a new database of noiseless images,then by inputting it into CNN,and combining with simulated annealing algorithm to improve training rate,finally building a model which can realize actual scence image denoising. Experimental results show that both gray images with noise,and images by camera achieve effectively smoothing effect,power signal-to-noise ratio (PSNR) value is high and image edges and details are preserved well.