计算机科学
人工智能
模式识别(心理学)
残余物
算法
噪音(视频)
降噪
图像质量
深度学习
卷积神经网络
块(置换群论)
图像(数学)
作者
Shengyu Li,Xuesong Liu,Rongxin Jiang,Fan Zhou,Yaowu Chen
标识
DOI:10.1117/1.jei.27.6.063005
摘要
Owing to recent advancements, very deep convolutional neural networks (CNNs) have found application in image denoising. However, while deeper models lead to better restoration performance, they are marred by a high number of parameters and increased training difficulty. To address these issues, we propose a CNN-based framework, named dilated residual encode–decode networks (DRED-Net), for image denoising, which learns direct end-to-end mappings from corrupted images to obtain clean images using few parameters. Our proposed network consists of multiple layers of convolution and deconvolution operators; in addition, we use dilated convolutions to boost the performance of our network without increasing the depth of the model or its complexity. Extensive experiments on synthetic noisy images are conducted to evaluate DRED-Net, and the results are compared with those obtained using state-of-the-art denoising methods. Our experimental results show that DRED-Net leads to results comparable with those obtained using other state-of-the-art methods for image denoising tasks.
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