The Fourth Challenge on Image Super-Resolution ($\times$4) at NTIRE 2026: Benchmark Results and Method Overview

arXiv cs.CV / 4/17/2026

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Key Points

  • The NTIRE 2026 Image Super-Resolution (×4) challenge at CVPR 2026 is introduced to develop methods that reconstruct high-resolution images from bicubically downsampled low-resolution inputs.
  • The benchmark targets two different goals with separate tracks: a restoration track ranked by PSNR for pixel-wise fidelity, and a perceptual track scored by visual realism using a perceptual metric.
  • The event drew 194 registered participants, with 31 teams submitting valid results for the reported benchmark.
  • The paper reports the full challenge setup, including design, datasets, evaluation protocol, main results, and an overview of methods used by participating teams.
  • By providing a unified benchmark aligned with evolving objectives, the challenge is intended to both assess current progress and guide future research directions in image super-resolution.

Abstract

This paper presents the NTIRE 2026 image super-resolution (\times4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a \times4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.