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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Image Denoising Networks with Residual Blocks and RReLUs
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Image Denoising Networks with Residual Blocks and RReLUs

机译：具有残留块和RReLU的图像去噪网络

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摘要

Discriminative learning methods have been widely studied in image denoising due to their swift inference and favorable performance. Nonetheless, their application range is greatly restricted by the specialized task (i.e., a specific model is required for each considered noise level), which prompts us to train a single network to tackle the blind image denoising problem. To this end, we take the advantages of state-of-the-art progress in deep learning to construct our denoising networks. Particularly, residual learning is utilized in our deep CNNs (convolutional neural networks) with pre-activation strategy to accelerate the training process. Furthermore, we employ RReLU (randomized leaky rectified linear unit) as the activation rather than the conventional use of ReLU (rectified linear unit). Extensive experiments are conducted to demonstrate that our model enjoys two desirable properties, including: (1) the ability to yield competitive denoising quality in comparison to specifically trained denoisers in several predetermined noise level and (2) the ability to handle a wide scope of noise levels effectively with a single network. The experimental results reveal its efficiency and effectiveness for image denoising tasks.
机译：判别性学习方法因其快速的推理和良好的性能而在图像去噪中得到了广泛的研究。但是，它们的应用范围受到专门任务的极大限制（即，每个考虑的噪声水平都需要一个特定的模型），这促使我们训练单个网络来解决盲目图像降噪问题。为此，我们利用深度学习领域的最新进展来构建我们的降噪网络。特别是，在我们的深层CNN（卷积神经网络）中利用预激活策略来利用残余学习来加快训练过程。此外，我们采用RReLU（随机泄漏整流线性单元）作为激活，而不是常规使用ReLU（整流线性单元）。进行了广泛的实验以证明我们的模型具有两个理想的属性，包括：（1）与在多个预定噪声水平上经过专门训练的去噪器相比，具有竞争性的去噪质量;以及（2）处理范围广泛的噪声的能力通过单个网络有效地升级。实验结果表明了其在图像去噪任务中的有效性和有效性。

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