One-to-many Approach for Improving Super-Resolution

Sieun Park, Caleb Eunho Lee

Keywords: Super resolution, Deep learning

TL;DR: We propose a piepline capable of generating multiple estimates of the reconstruction, and a loss not penalizing similar but equally realistic images for a one-to-many approach for super-resolution.

Abstract: Super-resolution (SR) is a one-to-many task with multiple possible solutions. However, previous works were not concerned about this characteristic. For a one-to-many pipeline, the generator should be able to generate multiple estimates of the reconstruction, and not be penalized for generating similar and equally realistic images. To achieve this, we propose adding weighted pixel-wise noise after every Residual-in-Residual Dense Block (RRDB) to enable the generator to generate various images. We modify the strict content loss to not penalize the stochastic variation in reconstructed images as long as it has consistent content. Additionally, we observe that there are out-of-focus regions in the DIV2K, DIV8K datasets that provide unhelpful guidelines. We filter blurry regions in the training data using the method of [10]. Finally, we modify the discriminator to receive the low-resolution image as a reference image along with the target image to provide better feedback to the generator. Using our proposed methods, we were able to improve the performance of ESRGAN in x4 perceptual SR and achieve the state-of-the-art LPIPS score in x16 perceptual extreme SR.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2106.10437/code)