NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
- URL: http://arxiv.org/abs/2205.05675v1
- Date: Wed, 11 May 2022 17:58:54 GMT
- Title: NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
- Abstract summary: The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of $times$4 based on pairs of low and corresponding high resolution images.
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Score: 279.8098140331206
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper reviews the NTIRE 2022 challenge on efficient single image
super-resolution with focus on the proposed solutions and results. The task of
the challenge was to super-resolve an input image with a magnification factor
of $\times$4 based on pairs of low and corresponding high resolution images.
The aim was to design a network for single image super-resolution that achieved
improvement of efficiency measured according to several metrics including
runtime, parameters, FLOPs, activations, and memory consumption while at least
maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the
baseline for efficiency measurement. The challenge had 3 tracks including the
main track (runtime), sub-track one (model complexity), and sub-track two
(overall performance). In the main track, the practical runtime performance of
the submissions was evaluated. The rank of the teams were determined directly
by the absolute value of the average runtime on the validation set and test
set. In sub-track one, the number of parameters and FLOPs were considered. And
the individual rankings of the two metrics were summed up to determine a final
ranking in this track. In sub-track two, all of the five metrics mentioned in
the description of the challenge including runtime, parameter count, FLOPs,
activations, and memory consumption were considered. Similar to sub-track one,
the rankings of five metrics were summed up to determine a final ranking. The
challenge had 303 registered participants, and 43 teams made valid submissions.
They gauge the state-of-the-art in efficient single image super-resolution.
Related papers
- The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report [122.8376730579832]
This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution.<n>The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs.<n>The challenge had 95 registered participants, and 15 teams made valid submissions.
arXiv Detail & Related papers (2026-04-03T17:17:26Z) - Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report [59.64053782297197]
This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge.<n>The challenge is based on a new test set based on the well known RSBlur dataset.<n>The top-performing approach achieved a PSNR of 31.1298 dB, showcasing the potential of efficient methods in this domain.
arXiv Detail & Related papers (2025-10-14T17:57:04Z) - NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results [159.15538432295656]
The NTIRE 2025 image super-resolution ($times$4) challenge is one of the associated competitions of the 10th NTIRE Workshop at CVPR 2025.
The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through bicubic downsampling with a $times$4 scaling factor.
A total of 286 participants registered for the competition, with 25 teams submitting valid entries.
arXiv Detail & Related papers (2025-04-20T12:08:22Z) - The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report [180.94772271910315]
This paper reviews the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions.
The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs.
The challenge had 262 registered participants, and 34 teams made valid submissions.
arXiv Detail & Related papers (2024-04-16T07:26:20Z) - NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results [126.78130602974319]
This paper reviews the NTIRE 2024 challenge on image super-resolution ($times$4)
The challenge involves generating corresponding high-resolution (HR) images, magnified by a factor of four, from low-resolution (LR) inputs.
The aim of the challenge is to obtain designs/solutions with the most advanced SR performance.
arXiv Detail & Related papers (2024-04-15T13:45:48Z) - NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results [173.32437855731752]
The challenge was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022.
The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations.
arXiv Detail & Related papers (2022-05-25T10:20:06Z) - NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results [116.77874476501913]
Given a noisy burst as input, the task in the challenge was to generate a clean RGB image with 4 times higher resolution.
The challenge contained two tracks; Track 1 evaluating on synthetically generated data, and Track 2 using real-world bursts from mobile camera.
The top-performing methods set a new state-of-the-art for the burst super-resolution task.
arXiv Detail & Related papers (2021-06-07T17:55:28Z) - AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results [222.26973543552327]
This paper reviews the AIM 2020 challenge on efficient single image super-resolution.
The challenge task was to super-resolve an input image with a magnification factor x4.
The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption.
arXiv Detail & Related papers (2020-09-15T09:25:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.