Image super-resolution is widely applied in face recognition, video perception, medical imaging, and many other fields. Although significant progress has been made, existing methods remain limited in reconstructing fine-grained texture details, making the pixels of the resulting images coarse. To address this problem, we propose a novel interpolation-based generative adversarial network (GAN) for high-resolution image reconstruction. First, an interpolation algorithm is introduced into the generator to carry out self-interpolation and channel interpolation using advanced features extracted from the low-resolution images. Second, the idea of residuals is introduced into both the generator and discriminator to expand the receptive field of the model and fully exploit the global features of the image jointly improving the visual perception of the resulting super-resolution image. Extensive experiments are conducted to evaluate the performances of the proposed models from two aspects: convergence speed and the resolution improvement effect. The experimental results demonstrate that the proposed model reaches a faster convergence speed with and comparable resolution improvement effect with respect to other state-of-the-art methods.

图像超分辨率已广泛应用于面部识别，视频感知，医学成像和许多其他领域。尽管已经取得了重大进展，但是现有方法在重建细粒度纹理细节方面仍然受到限制，从而使所得图像的像素变得粗糙。为了解决这个问题，我们提出了一种新颖的基于插值的生成对抗网络（GAN）用于高分辨率图像重建。首先，将插值算法引入生成器，以使用从低分辨率图像中提取的高级特征执行自插值和通道插值。第二，残差的思想同时被引入到生成器和鉴别器中，以扩展模型的接收范围并充分利用图像的全局特征，从而共同改善所得超分辨率图像的视觉感知。进行了广泛的实验，从收敛速度和分辨率提高效果两个方面评估了所提出模型的性能。实验结果表明，与其他最新方法相比，所提出的模型具有更快的收敛速度和可比的分辨率改进效果。