NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results (2005.01996v1)

Abstract: This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Processing artifacts, the aim is to super-resolve images with synthetically generated image processing artifacts. This allows for quantitative benchmarking of the approaches \wrt a ground-truth image. In Track 2: Smartphone Images, real low-quality smart phone images have to be super-resolved. In both tracks, the ultimate goal is to achieve the best perceptual quality, evaluated using a human study. This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019. In total 22 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem.

Overview of the NTIRE 2020 Challenge on Real-World Image Super-Resolution

The NTIRE 2020 Challenge on Real-World Image Super-Resolution addressed the challenging problem of improving image resolution without paired high and low-resolution training data. This competition focused on strategies for super-resolution that accommodate real-world settings where individual image formation models might be unknown and training data consists of unpaired, real images. The challenge featured two tracks: Track 1 aimed to tackle images with processing artifacts, while Track 2 focused on smartphone images.

Challenge Structure

The NTIRE Challenge is structured to promote advancements in super-resolution technologies through unsupervised and weakly-supervised approaches. Participants were tasked with achieving enhancements in image quality under the constraint of unpaired training images, utilizing innovative methods to overcome barriers presented by mismatched high and low-resolution pairs. The challenge collected contributions from 22 teams, each demonstrating new approaches to real-world super-resolution problems.

Participating Methods

Prominent approaches from participating teams can be categorized based on their overarching strategies. Many teams employed a two-step approach: first learning or simulating the degradation process present in real images and then synthesizing paired data for training a super-resolution model. Methods such as DSGAN and CycleGAN typified this two-step pipeline, focusing on generating realistic degraded versions of high-resolution images to better train supervised models. Experimentation was notable in the degradation modeling phase, where tools like KernelGAN were employed to estimate image blur and noise characteristics, achieving noteworthy results in enhancing image fidelity.

Challenge Results

For Track 1, the paper reports significant improvement compared to its predecessor challenges, with more teams achieving better MOS scores than standard bicubic interpolation. In Track 2, methods such as those from the Impressionism team demonstrated major improvements by explicitly modeling kernel estimations using KernelGAN, achieving superior sharpness and detail restoration. In both tracks, novel strategies to accommodate the absence of paired data led to remarkable progress in perceptual image quality.

Implications and Future Work

The implications of the NTIRE 2020 Challenge are broad, demonstrating significant advancements in unpaired learning for image enhancement. The participating methods serve as benchmarks and open up further research pathways into unsupervised domain adaptation, generative modeling, and cycle-consistent network applications. Future developments are anticipated to extend beyond image super-resolution into other visual applications where paired data is scarce. Continued innovation in real-world image processing may yield robust, machine-based solutions for various downstream applications, enhancing image quality in consumer devices and professional imaging systems alike.

The NTIRE 2020 Challenge underscores the importance of crafting novel, practical approaches for high-quality image reconstruction in scenarios where traditional training data is unavailable, marking a step forward in real-world image processing capabilities.