mlsanityData to 7 Oct 2026

AIM 2022 Challenge on Instagram Filter Removal: Methods and Results

arXiv 2210.08997 ↗First posted 17 Oct 2022cs.CV cs.LG eess.IVcited by 6

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Abstract (arXiv)

This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. Social media filters transform the images by consecutive non-linear operations, and the feature maps of the original content may be interpolated into a different domain. This reduces the overall performance of the recent deep learning strategies. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. The proposed solutions are ranked in terms of the PSNR value with respect to the original images. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge. The comparison of qualitative results of the proposed solutions and the benchmark for the challenge are presented in this report.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 38 references that are in the map. First posted on arXiv 17 Oct 2022.

Builds on

23 of 38 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2022: 6.

6 papers.

  1. 2022Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Reportcited by 16
  2. 2022Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Reportcited by 13
  3. 2022Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Reportcited by 11
  4. 2022Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Reportcited by 4
  5. 2022Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Reportcited by 3
  6. 2022Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Reportcited by 2