Abstract
Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational burden, which limits their practical usages. We believe that some corrupted image regions are inherently easier to restore than others since the distortion and content vary within an image. To this end, we propose Path-Restore, a multi-path CNN with a pathfinder that could dynamically select an appropriate route for each image region. We train the pathfinder using reinforcement learning with a difficulty-regulated reward, which is related to the performance, complexity and "the difficulty of restoring a region". We conduct experiments on denoising and mixed restoration tasks. The results show that our method could achieve comparable or superior performance to existing approaches with less computational cost. In particular, our method is effective for real-world denoising, where the noise distribution varies across different regions of a single image. We surpass the state-of-the-art CBDNet by 0.94 dB and run 29% faster on the realistic Darmstadt Noise Dataset. Models and codes will be released.
Abstract (translated)
非常深的卷积神经网络（CNN）大大提高了各种图像恢复任务的性能。然而，这是以增加计算负担为代价的，这限制了它们的实际应用。我们认为，由于图像中的失真和内容不同，一些损坏的图像区域天生比其他区域更容易恢复。为此，我们提出了路径恢复，一个多路径CNN与一个探路者，可以动态地为每个图像区域选择适当的路径。我们使用强化学习来训练探路者，并以一种难度调节的奖励，这与性能、复杂性和“恢复一个区域的难度”有关。我们对去噪和混合修复任务进行了实验。结果表明，该方法与现有方法相比，具有可比性或优越性，计算成本较低。特别是，我们的方法对于真实世界的去噪是有效的，在那里噪声分布在单个图像的不同区域不同。我们超过最先进的cbdnet 0.94db，在现实的darmstadt噪声数据集上运行速度快29%。将发布型号和代码。
URL
https://arxiv.org/abs/1904.10343
