mlsanityData to 7 Oct 2026

PyNET-CA: Enhanced PyNET with Channel Attention for End-to-End Mobile Image Signal Processing

arXiv 2104.02895 ↗First posted 7 Apr 2021eess.IV cs.CV cs.LGcited by 13

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Abstract (arXiv)

Reconstructing RGB image from RAW data obtained with a mobile device is related to a number of image signal processing (ISP) tasks, such as demosaicing, denoising, etc. Deep neural networks have shown promising results over hand-crafted ISP algorithms on solving these tasks separately, or even replacing the whole reconstruction process with one model. Here, we propose PyNET-CA, an end-to-end mobile ISP deep learning algorithm for RAW to RGB reconstruction. The model enhances PyNET, a recently proposed state-of-the-art model for mobile ISP, and improve its performance with channel attention and subpixel reconstruction module. We demonstrate the performance of the proposed method with comparative experiments and results from the AIM 2020 learned smartphone ISP challenge. The source code of our implementation is available at https://github.com/egyptdj/skyb-aim2020-public

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 19 of its 21 references that are in the map. First posted on arXiv 7 Apr 2021.

Builds on

19 of 21 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
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7 more
  1. 2017DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networkscited by 156
  2. 2020Replacing Mobile Camera ISP with a Single Deep Learning Modelcited by 108
  3. 2018DeepISP: Towards Learning an End-to-End Image Processing Pipelinecited by 88
  4. 2020AIM 2020 Challenge on Learned Image Signal Processing Pipelinecited by 44
  5. 2019Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolutioncited by 30
  6. 2019HighEr-Resolution Network for Image Demosaicing and Enhancingcited by 11
  7. 2019W-Net: Two-stage U-Net with misaligned data for raw-to-RGB mappingcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 7; 2023: 2; 2024: 0; 2025: 1; 2026: 1.

13 papers.

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  1. 2021Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Reportcited by 34
  2. 2022Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Reportcited by 13
  3. 2022MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learningcited by 13
  4. 2022MIPI 2022 Challenge on Quad-Bayer Re-mosaic: Dataset and Reportcited by 9
  5. 2022PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networkscited by 7
  6. 2023Efficient Unified Demosaicing for Bayer and Non-Bayer Patterned Image Sensorscited by 6
  7. 2021Overparametrization of HyperNetworks at Fixed FLOP-Count Enables Fast Neural Image Enhancementcited by 4
  8. 2025Learned Lightweight Smartphone ISP with Unpaired Datacited by 3
  9. 2022PyNET-QxQ: An Efficient PyNET Variant for QxQ Bayer Pattern Demosaicing in CMOS Image Sensorscited by 2
  10. 2022CRISPnet: Color Rendition ISP Netcited by 1
  11. 2022LW-ISP: A Lightweight Model with ISP and Deep Learningcited by 1
  12. 2023ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processingcited by 1
1 more
  1. 2026RPBA-Net: An Interpretable Residual Pyramid Bilateral Affine Network for RAW-Domain ISP Enhancementcited by 0