In many real-world cases such as printer devices and in-camera interpolation, only the interpolated versions of the low-resolution images are available. In this paper, a new low-complexity high-performance image super resolution network is proposed that starting from the bicubic interpolated version of the low resolution image produces a high quality super resolved image. The main idea in the proposed scheme is the development of a feature generating block that is capable of producing features using multiple local spatial ranges and multiple resolution levels, fusing them in order to provide a rich set of feature maps, and using them in a recursive framework. The objective in designing such a recursive block is not simply to provide a light-weight network, as is traditionally done in the design of such a network, but also to provide a low count on the number of multiply-accumulate operations with high performance. The experimental results are provided to show that the proposed network outperforms other recursive super resolution networks when their super resolution capability, the number of parameters and number of multiply-accumulate operations are simultaneously taken into consideration.

在许多实际情况下，例如打印机设备和相机内插，只有低分辨率图像的插值版本可用。本文提出了一种新的低复杂度高性能图像超分辨率网络，该网络从低分辨率图像的双三次插值版本开始可生成高质量的超分辨图像。提出的方案中的主要思想是开发一种特征生成模块，该模块能够使用多个局部空间范围和多个分辨率级别生成特征，将其融合以提供丰富的特征图集，并在递归中使用它们框架。设计这样的递归块的目的不仅仅是像传统上在设计这样的网络时那样提供轻量级的网络，而且还可以减少高性能的乘法累加运算的次数。实验结果表明，在同时考虑其超分辨能力，参数数量和多次累加运算数量的情况下，所提出的网络优于其他递归超分辨率网络。