Feature-level interpolation-based GAN for image super-resolution

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.