Image denoising is a challenging inverse problem due to complex scenes and information loss. Recently, various methods have been considered to solve this problem by building a well-designed convolutional neural network (CNN) or introducing some hand-designed image priors. Different from previous works, we investigate a new framework for image denoising, which integrates edge detection, edge guidance, and image denoising into an end-to-end CNN model. To achieve this goal, we propose a multilevel edge features guided network (MLEFGN). First, we build an edge reconstruction network (Edge-Net) to directly predict clear edges from the noisy image. Then, the Edge-Net is embedded as part of the model to provide edge priors, and a dual-path network is applied to extract the image and edge features, respectively. Finally, we introduce a multilevel edge features guidance mechanism for image denoising. To the best of our knowledge, the Edge-Net is the first CNN model specially designed to reconstruct image edges from the noisy image, which shows good accuracy and robustness on natural images. Extensive experiments clearly illustrate that our MLEFGN achieves favorable performance against other methods and plenty of ablation studies demonstrate the effectiveness of our proposed Edge-Net and MLEFGN. The code is available at https://github.com/MIVRC/MLEFGN-PyTorch .

中文翻译：

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用于图像去噪的多级边缘特征引导网络

由于复杂的场景和信息丢失，图像去噪是一个具有挑战性的逆问题。最近，已经考虑通过构建精心设计的卷积神经网络 (CNN) 或引入一些手工设计的图像先验来解决此问题的各种方法。与之前的工作不同，我们研究了一种新的图像去噪框架，它将边缘检测、边缘引导和图像去噪集成到一个端到端的 CNN 模型中。为了实现这一目标，我们提出了一个多级边缘特征引导网络（MLEFGN）。首先，我们构建了一个边缘重建网络（Edge-Net）来直接从嘈杂的图像中预测清晰的边缘。然后，将 Edge-Net 作为模型的一部分嵌入以提供边缘先验，并应用双路径网络分别提取图像和边缘特征。最后，我们引入了一种用于图像去噪的多级边缘特征引导机制。据我们所知，Edge-Net 是第一个专门用于从噪声图像重建图像边缘的 CNN 模型，在自然图像上显示出良好的准确性和鲁棒性。大量实验清楚地表明，我们的 MLEFGN 相对于其他方法取得了良好的性能，大量的消融研究证明了我们提出的 Edge-Net 和 MLEFGN 的有效性。该代码可在 大量实验清楚地表明，我们的 MLEFGN 相对于其他方法取得了良好的性能，大量的消融研究证明了我们提出的 Edge-Net 和 MLEFGN 的有效性。该代码可在 大量实验清楚地表明，我们的 MLEFGN 相对于其他方法取得了良好的性能，大量的消融研究证明了我们提出的 Edge-Net 和 MLEFGN 的有效性。该代码可在https://github.com/MIVRC/MLEFGN-PyTorch .