The physical process used in CCD cameras for image formation makes it imperative to simultaneously upsample and deblur the captured images. In this paper, we provide an efficient scheme to solve this problem through a nonlinear end-to-end mapping carried out by a novel deep light-weight residual neural network. The proposed network is designed based on two main modules, namely, image upsampling and image deblurring, aimed for carrying out simultaneously the two tasks involved with the problem. The proposed network employs a residual block with a capacity of generating features in multiple receptive fields and enhancing the network's representational capability. The proposed network is extensively experimented using benchmark datasets for image restoration.

中文翻译：

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UPDResNN：深度轻量级图像上采样和去模糊残差神经网络


CCD 相机用于图像形成的物理过程使得必须同时对捕获的图像进行上采样和去模糊。在本文中，我们提供了一种有效的方案，通过由新型深度轻量级残差神经网络执行的非线性端到端映射来解决该问题。所提出的网络是基于两个主要模块设计的，即图像上采样和图像去模糊，旨在同时执行与问题相关的两个任务。所提出的网络采用残差块，能够在多个感受野中生成特征并增强网络的表示能力。使用图像恢复的基准数据集对所提出的网络进行了广泛的实验。