MULTIRESOLUTION MIXTURE GENERATIVE ADVERSARIAL NETWORK FOR IMAGE SUPER-RESOLUTION

With regard to the problem of image super-resolution (SR), generative adversarial network (GAN) can make generated images have more details and better effect on perceptual quality than other methods. However, GAN-based methods may lose the contour of object in some texture-intensive areas. In order to recover contour better and further enhance perceptual quality, we propose a Multiresolution Mixture Generative Adversarial Network for Image Super-Resolution (MRMGAN), which employs a multiresolution mixture network (MRMNet) for image super-resolution. The MRMNet is able to have multiple resolution feature maps at the same time when training. Meanwhile, we propose a residual fluctuation loss, which aims to reduce the overall fluctuation of residual between SR image and high-resolution (HR) image. We evaluated the proposed method on benchmark datasets. Experimental results show that the proposed MRMGAN can get satisfactory performance.