KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

J. Fu, Hong Wang, Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu
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引用次数: 10

Abstract

Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet.
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KXNet:一个模型驱动的盲超分辨率深度神经网络
虽然目前基于深度学习的方法在盲单图像超分辨率(SISR)任务中取得了很好的效果,但大多数方法主要集中在启发式地构建不同的网络架构,而对模糊核与高分辨率(HR)图像之间物理生成机制的显式嵌入重视较少。为了缓解这个问题,我们提出了一个模型驱动的深度神经网络,称为KXNet,用于盲SISR。具体来说,针对经典的SISR模型,我们提出了一种简单有效的迭代算法。然后,通过将所涉及的迭代步骤展开到相应的网络模块中,我们自然地构建了KXNet。所提出的KXNet的主要特点是,整个学习过程完全且明确地集成了作为SISR任务基础的内在物理机制。因此,学习到的模糊内核具有清晰的物理模式,模糊内核与HR图像之间的相互迭代过程可以很好地引导KXNet朝着正确的方向进化。在合成数据和真实数据上进行的大量实验很好地证明了我们的方法优于当前具有代表性的最先进的盲SISR方法的准确性和通用性。代码可从https://github.com/jiahong-fu/KXNet获得。
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