Generally, there are mainly two methods to solve the image restoration task in low-level computer vision, i.e., the model-based optimization method and the discriminative learning method. However, these two methods have clear advantages and disadvantages. For example, it is flexible for the model-based optimization method to handle different problems, but large quantity of computing time is required for better performance. The discriminative learning approach has high computing efficiency, but the application scope is seriously limited by the fixed training model. It would be better to combine the advantages of these two methods. Luckily, with the variable splitting techniques, we insert the trained convolutional neural network (CNN) for denoising as one model to the model-based optimization method to solve other image restoration problems (e.g., deblurring and super-resolution). Final experimental results show that our denoising network is able to provide strong prior information for image restoration tasks. The image restoration effects can reach or approximate the most advanced algorithm in such three tasks as denoising, deblurring, and super-resolution. Moreover, the algorithm proposed in this paper is also the most competitive in terms of computational efficiency.

中文翻译：

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卷积神经网络与半二次分裂相结合的图像复原

通常，解决基于低级计算机视觉的图像恢复任务的方法主要有两种，即基于模型的优化方法和判别学习方法。但是，这两种方法都有明显的优缺点。例如，基于模型的优化方法可以灵活地处理各种问题，但是需要大量的计算时间才能获得更好的性能。判别式学习方法具有较高的计算效率，但其应用范围受到固定训练模型的严重限制。最好结合这两种方法的优点。幸运的是，借助变量拆分技术，我们将用于降噪的训练卷积神经网络（CNN）作为一种模型插入了基于模型的优化方法中，以解决其他图像恢复问题（例如，去模糊和超分辨率）。最终的实验结果表明，我们的降噪网络能够为图像恢复任务提供强大的先验信息。在去噪，去模糊和超分辨率这三个任务中，图像恢复效果可以达到或接近最先进的算法。此外，本文提出的算法在计算效率方面也是最具竞争力的。