Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques
This addresses the issue of missing fine details and training instability in SISR for applications like image enhancement, though it appears incremental as it builds on existing CNN and GAN approaches.
The paper tackles the problem of generating high-resolution images with fine details in Single Image Super-Resolution (SISR) by proposing a method where CNNs align images in different spaces using convex optimization techniques, resulting in stable training and recovery of sharp details without artifacts.
Single Image Super-Resolution (SISR) task refers to learn a mapping from low-resolution images to the corresponding high-resolution ones. This task is known to be extremely difficult since it is an ill-posed problem. Recently, Convolutional Neural Networks (CNNs) have achieved state of the art performance on SISR. However, the images produced by CNNs do not contain fine details of the images. Generative Adversarial Networks (GANs) aim to solve this issue and recover sharp details. Nevertheless, GANs are notoriously difficult to train. Besides that, they generate artifacts in the high-resolution images. In this paper, we have proposed a method in which CNNs try to align images in different spaces rather than only the pixel space. Such a space is designed using convex optimization techniques. CNNs are encouraged to learn high-frequency components of the images as well as low-frequency components. We have shown that the proposed method can recover fine details of the images and it is stable in the training process.