Image denoising is a thoroughly studied research problem in the areas of image processing and computer vision. In this work, a deep convolution neural network with added benefits of residual learning for image denoising is proposed. The network is composed of convolution layers and ResNet blocks along with rectified linear unit activation functions. The network is capable of learning end-to-end mappings from noise distorted images to restored cleaner versions. The deeper networks tend to be challenging to train and often are posed with the problem of vanishing gradients. The residual learning and orthogonal kernel initialisation keep the gradients in check. The skip connections in the ResNet blocks pass on the learned abstractions further down the network in the forward pass, thus achieving better results. With a single model, one can tackle different levels of Gaussian noise efficiently. The experiments conducted on the benchmark datasets prove that the proposed model obtains a significant improvement in structural similarity index than the previously existing state-of-the-art techniques.

中文翻译：

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ResDNN：深度残差学习，实现自然图像降噪

图像降噪是在图像处理和计算机视觉领域中经过深入研究的研究问题。在这项工作中，提出了一种深度卷积神经网络，该网络具有残差学习的附加优点，可用于图像去噪。该网络由卷积层和ResNet块以及经过校正的线性单元激活功能组成。该网络能够学习从噪声失真的图像到还原的更干净版本的端到端映射。较深的网络往往难以训练，并且经常面临梯度消失的问题。残差学习和正交核初始化使梯度得到控制。ResNet块中的跳过连接在前向传递中沿网络进一步传递了学习到的抽象，从而获得了更好的结果。对于单个模型，一个人可以有效地解决不同级别的高斯噪声。在基准数据集上进行的实验证明，与以前现有的最新技术相比，所提出的模型在结构相似性指标上获得了显着改善。