While some denoising methods based on deep learning achieve superior results on synthetic noise, they are far from dealing with photographs corrupted by realistic noise. Denoising on real-world noisy images faces more significant challenges due to the source of it is more complicated than synthetic noise. To address this issue, we propose a novel network including noise estimation module and removal module (NERNet). The noise estimation module automatically estimates the noise level map corresponding to the information extracted by symmetric dilated block and pyramid feature fusion block. The removal module focuses on removing the noise from the noisy input with the help of the estimated noise level map. Dilation selective block with attention mechanism in the removal module adaptively not only fuses features from convolution layers with different dilation rates, but also aggregates the global and local information, which is benefit to preserving more details and textures. Experiments on two datasets of synthetic noise and three datasets of realistic noise show that NERNet achieves competitive results in comparison with other state-of-the-art methods.

尽管一些基于深度学习的去噪方法在合成噪点上取得了优异的效果，但它们远不能处理被现实噪点破坏的照片。由于真实噪声图像的来源比合成噪声更复杂，因此对真实噪声图像进行去噪面临更大的挑战。为了解决这个问题，我们提出了一种新颖的网络，其中包括噪声估计模块和消除模块（NERNet）。噪声估计模块自动估计与对称膨胀块和金字塔特征融合块提取的信息相对应的噪声水平图。去除模块致力于借助估计的噪声水平图从噪声输入中去除噪声。去除模块中具有注意机制的扩张选择块不仅自适应融合了具有不同扩张率的卷积层的特征，而且还汇总了全局和局部信息，有利于保留更多的细节和纹理。在两个合成噪声数据集和三个实际噪声数据集上进行的实验表明，与其他最新方法相比，NERNet取得了竞争性结果。