Parametric regularization loss in super-resolution reconstruction

A noise-enhanced super-resolution generative adversarial network plus (nESRGAN+) was proposed to improve the enhanced super-resolution GAN (ESRGAN). The contributions of nESRGAN+ generate an impressive reconstructed image with more texture details and greater sharpness. However, the perceptual quality of the output lacks hallucinated details and undesirable artifacts and takes a long time to converge. To address these problems, we propose four types of parametric regularization algorithms as loss functions of the model to enable the iterative weight adjustment of the network gradient. Several experiments were conducted to confirm that the generator can achieve a better-quality reconstructed image, including restoring the unseen texture. Our method accomplished the average peak signal-to-noise ratio (PSNR) of the reconstructed image at 27.96 dB, the average Structural Similarity Index Measure (SSIM) at 0.8303, and the average Learned Perceptual Image Patch Similarity (LPIPS) at 0.1949. It took seven times less training time than the state of the art. In addition to the better visual quality of the reconstructed result, the proposed loss functions allow the generator to converge faster.