Image Super-Resolution with Perceptual Quality Assessment Guidance

What is it about?

Generative Adversarial Networks (GAN) have demonstrated the potential to recover realistic details for single image super-resolution (SISR). However, without considering perceptual metrics in optimization, existing perceptual SR methods could not show stable performance. To further improve the visual quality of super-resolution results, we propose a SISR method driven by image perceptual quality assessment. Through evaluating the generation results of GAN-based SISR and utilizing the evaluation results as the loss function, the generator can be optimized to obtain results with better perceptual quality.

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