Real Image Super-Resolution using GAN through modeling of LR and HR process
This work addresses the challenge of super-resolution for real-world images with complex degradations, representing an incremental improvement over existing methods.
The paper tackles the problem of real image super-resolution by addressing the mismatch between ideal bicubic downsampling and real-world degradations, proposing a GAN-based approach that learns degradation distributions to synthesize training data and achieves effectiveness in experiments.
The current existing deep image super-resolution methods usually assume that a Low Resolution (LR) image is bicubicly downscaled of a High Resolution (HR) image. However, such an ideal bicubic downsampling process is different from the real LR degradations, which usually come from complicated combinations of different degradation processes, such as camera blur, sensor noise, sharpening artifacts, JPEG compression, and further image editing, and several times image transmission over the internet and unpredictable noises. It leads to the highly ill-posed nature of the inverse upscaling problem. To address these issues, we propose a GAN-based SR approach with learnable adaptive sinusoidal nonlinearities incorporated in LR and SR models by directly learn degradation distributions and then synthesize paired LR/HR training data to train the generalized SR model to real image degradations. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments.