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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 Workshops pp 44–70Cite as

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13803))

Abstract

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon’s 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20–50 ms while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

Andrey Ignatov and Radu Timofte are the main Mobile AI & AIM 2022 challenge organizers. The other authors participated in the challenge.

Mobile AI 2022 Workshop website:

https://ai-benchmark.com/workshops/mai/2022/

Appendix A contains the authors’ team names and affiliations.

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Notes

  1. 1.

    https://ai-benchmark.com/download.

  2. 2.

    https://play.google.com/store/apps/details?id=org.benchmark.demo.

References

  1. Afifi, M., Brubaker, M.A., Brown, M.S.: HistoGAN: controlling colors of GAN-generated and real images via color histograms. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7941–7950 (2021)

    Google Scholar 

  2. Cai, J., Gu, S., Timofte, R., Zhang, L.: Ntire 2019 challenge on real image super-resolution: Methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  3. Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M.W., Keutzer, K.: Zeroq: A novel zero shot quantization framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13169–13178 (2020)

    Google Scholar 

  4. Chiang, C.M., et al.: Deploying image deblurring across mobile devices: a perspective of quality and latency. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 502–503 (2020)

    Google Scholar 

  5. Conde, M.V., McDonagh, S., Maggioni, M., Leonardis, A., Pérez-Pellitero, E.: Model-based image signal processors via learnable dictionaries. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 481–489 (2022)

    Google Scholar 

  6. Conde, M.V., Timofte, R., et al.: Reversed image signal processing and RAW reconstruction. AIM 2022 challenge report. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)

    Google Scholar 

  7. Dai, L., Liu, X., Li, C., Chen, J.: AWNET: Attentive wavelet network for image ISP. arXiv preprint arXiv:2008.09228 (2020)

  8. Ding, X., et al.: ResRep: lossless CNN pruning via decoupling remembering and forgetting. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4510–4520 (2021)

    Google Scholar 

  9. Ding, X., Xia, C., Zhang, X., Chu, X., Han, J., Ding, G.: RepMLP: Re-parameterizing convolutions into fully-connected layers for image recognition. arXiv preprint arXiv:2105.01883 (2021)

  10. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., Sun, J.: RepVGG: making VGG-style convnets great again. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13733–13742 (2021)

    Google Scholar 

  11. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)

    Google Scholar 

  12. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2030–2096 (2016)

    MathSciNet  MATH  Google Scholar 

  13. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009 (2022)

    Google Scholar 

  14. Howard, A., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)

    Google Scholar 

  15. Huang, J., et al.: Range scaling global U-Net for perceptual image enhancement on mobile devices. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 230–242. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_15

    Chapter  Google Scholar 

  16. Hui, Z., Wang, X., Deng, L., Gao, X.: Perception-preserving convolutional networks for image enhancement on smartphones. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  17. Ignatov, A., Byeoung-su, K., Timofte, R.: Fast camera image denoising on mobile GPUs with deep learning, mobile AI 2021 challenge: Report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  18. Ignatov, A., Chiang, J., Kuo, H.K., Sycheva, A., Timofte, R.: Learned smartphone isp on mobile NPUs with deep learning, mobile AI 2021 challenge: Report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  19. Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3277–3285 (2017)

    Google Scholar 

  20. Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: WESPE: weakly supervised photo enhancer for digital cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 691–700 (2018)

    Google Scholar 

  21. Ignatov, A., Malivenko, G., Plowman, D., Shukla, S., Timofte, R.: Fast and accurate single-image depth estimation on mobile devices, mobile AI 2021 challenge: Report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  22. Ignatov, A., Malivenko, G., Timofte, R.: Fast and accurate quantized camera scene detection on smartphones, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  23. Ignatov, A., et al.: PyNet-V2 mobile: efficient on-device photo processing with neural networks. In: 2021 26th International Conference on Pattern Recognition (ICPR). IEEE (2022)

    Google Scholar 

  24. Ignatov, A., Malivenko, G., Timofte, R., et al.: Efficient single-image depth estimation on mobile devices, mobile AI & AIM 2022 challenge: report. In: European Conference on Computer Vision (2022)

    Google Scholar 

  25. Ignatov, A., Patel, J., Timofte, R.: Rendering natural camera bokeh effect with deep learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 418–419 (2020)

    Google Scholar 

  26. Ignatov, A., et al.: Aim 2019 challenge on bokeh effect synthesis: methods and results. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3591–3598. IEEE (2019)

    Google Scholar 

  27. Ignatov, A., et al.: MicroISP: processing 32mp photos on mobile devices with deep learning. In: European Conference on Computer Vision (2022)

    Google Scholar 

  28. Ignatov, A., Timofte, R.: Ntire 2019 challenge on image enhancement: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  29. Ignatov, A., et al.: Power efficient video super-resolution on mobile NPUs with deep learning, mobile AI & AIM 2022 challenge: report. In: European Conference on Computer Vision (2022)

    Google Scholar 

  30. Ignatov, A., et al.: AI benchmark: running deep neural networks on android smartphones. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 288–314. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_19

    Chapter  Google Scholar 

  31. Ignatov, A., Timofte, R., Denna, M., Younes, A.: Real-time quantized image super-resolution on mobile NPUs, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  32. Ignatov, A., Timofte, R., Denna, M., Younes, A., et al.: Efficient and accurate quantized image super-resolution on mobile NPUs, mobile AI & AIM 2022 challenge: report. In: European Conference on Computer Vision (2022)

    Google Scholar 

  33. Ignatov, A., et al.: Aim 2019 challenge on raw to RGB mapping: methods and results. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3584–3590. IEEE (2019)

    Google Scholar 

  34. Ignatov, A., et al.: AI benchmark: all about deep learning on smartphones in 2019. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3617–3635. IEEE (2019)

    Google Scholar 

  35. Ignatov, A., et al.: AIM 2020 challenge on rendering realistic Bokeh. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 213–228. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_13

    Chapter  Google Scholar 

  36. Ignatov, A., et al.: PIRM challenge on perceptual image enhancement on smartphones: report. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  37. Ignatov, A., et al.: AIM 2020 challenge on learned image signal processing pipeline. arXiv preprint arXiv:2011.04994 (2020)

  38. Ignatov, A., Timofte, R., et al.: Realistic bokeh effect rendering on mobile GPUs, mobile AI & AIM 2022 challenge: report (2022)

    Google Scholar 

  39. Ignatov, A., Van Gool, L., Timofte, R.: Replacing mobile camera ISP with a single deep learning model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 536–537 (2020)

    Google Scholar 

  40. Ignatov, D., Ignatov, A.: Controlling information capacity of binary neural network. Pattern Recogn. Lett. 138, 276–281 (2020)

    Article  Google Scholar 

  41. Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704–2713 (2018)

    Google Scholar 

  42. Jain, S.R., Gural, A., Wu, M., Dick, C.H.: Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks. arXiv preprint arXiv:1903.08066 (2019)

  43. Kim, B.-H., Song, J., Ye, J.C., Baek, J.H.: PyNET-CA: enhanced PyNET with channel attention for end-to-end mobile image signal processing. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 202–212. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_12

    Chapter  Google Scholar 

  44. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  45. Kınlı, F.O., Menteş, S., Özcan, B., Kirac, F., Timofte, R., et al.: AIM 2022 challenge on Instagram filter removal: Methods and results. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)

    Google Scholar 

  46. Lee, J.,et al.: On-device neural net inference with mobile GPUs. arXiv preprint arXiv:1907.01989 (2019)

  47. Li, Y., Gu, S., Gool, L.V., Timofte, R.: Learning filter basis for convolutional neural network compression. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5623–5632 (2019)

    Google Scholar 

  48. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)

    Google Scholar 

  49. Liu, H., Navarrete Michelini, P., Zhu, D.: Deep networks for image-to-image translation with Mux and Demux layers. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 150–165. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_10

    Chapter  Google Scholar 

  50. Liu, J., Tang, J., Wu, G.: Residual feature distillation network for lightweight image super-resolution. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12537, pp. 41–55. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-67070-2_2

    Chapter  Google Scholar 

  51. Liu, Z., et al.: Metapruning: Meta learning for automatic neural network channel pruning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3296–3305 (2019)

    Google Scholar 

  52. Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.T.: Bi-Real net: enhancing the performance of 1-bit CNNs with improved representational capability and advanced training algorithm. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 722–737 (2018)

    Google Scholar 

  53. Lugmayr, A., Danelljan, M., Timofte, R.: Unsupervised learning for real-world super-resolution. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3408–3416. IEEE (2019)

    Google Scholar 

  54. Lugmayr, A., Danelljan, M., Timofte, R.: Ntire 2020 challenge on real-world image super-resolution: methods and results. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 494–495 (2020)

    Google Scholar 

  55. Obukhov, A., Rakhuba, M., Georgoulis, S., Kanakis, M., Dai, D., Van Gool, L.: T-basis: a compact representation for neural networks. In: International Conference on Machine Learning, pp. 7392–7404. PMLR (2020)

    Google Scholar 

  56. Real, E., Aggarwal, A., Huang, Y., Le, Q.V.: Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4780–4789 (2019)

    Google Scholar 

  57. Romero, A., Ignatov, A., Kim, H., Timofte, R.: Real-time video super-resolution on smartphones with deep learning, mobile AI 2021 challenge: report. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (2021)

    Google Scholar 

  58. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  59. Seif, G., Androutsos, D.: Edge-based loss function for single image super-resolution. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1468–1472. IEEE (2018)

    Google Scholar 

  60. Silva, J.I.S., et al.: A deep learning approach to mobile camera image signal processing. In: Anais Estendidos do XXXIII Conference on Graphics, Patterns and Images, pp. 225–231. SBC (2020)

    Google Scholar 

  61. de Stoutz, E., Ignatov, A., Kobyshev, N., Timofte, R., Van Gool, L.: Fast perceptual image enhancement. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2018)

    Google Scholar 

  62. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  63. Tan, M., et al.: MNASNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2820–2828 (2019)

    Google Scholar 

  64. TensorFlow-Lite. https://www.tensorflow.org/lite

  65. Timofte, R., Gu, S., Wu, J., Van Gool, L.: Ntire 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 852–863 (2018)

    Google Scholar 

  66. Truong, P., Danelljan, M., Van Gool, L., Timofte, R.: Learning accurate dense correspondences and when to trust them. arXiv preprint arXiv:2101.01710 (2021)

  67. Uhlich, S., et al.: Mixed precision DNNs: All you need is a good parametrization. arXiv preprint arXiv:1905.11452 (2019)

  68. Vu, T., Nguyen, C.V., Pham, T.X., Luu, T.M., Yoo, C.D.: Fast and efficient image quality enhancement via DesubPixel convolutional neural networks. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 243–259. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_16

    Chapter  Google Scholar 

  69. Wan, A., et al.: FBNetV2: differentiable neural architecture search for spatial and channel dimensions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12965–12974 (2020)

    Google Scholar 

  70. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1

    Chapter  Google Scholar 

  71. Wu, Y., Zheng, J., Fan, Z., Wu, X., Zhang, F.: Residual feature distillation channel spatial attention network for ISP on smartphone. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)

    Google Scholar 

  72. Wu, B., et al.: FBNet: hardware-aware efficient convnet design via differentiable neural architecture search. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10734–10742 (2019)

    Google Scholar 

  73. Yang, J., et al.: Quantization networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7308–7316 (2019)

    Google Scholar 

  74. Yang, R., Timofte, R., et al.: AIM 2022 challenge on super-resolution of compressed image and video: dataset, methods and results. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops (2022)

    Google Scholar 

  75. Zhang, X., Zeng, H., Zhang, L.: Edge-oriented convolution block for real-time super resolution on mobile devices. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4034–4043 (2021)

    Google Scholar 

  76. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)

    Google Scholar 

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Acknowledgements

We thank the sponsors of the Mobile AI and AIM 2022 workshops and challenges: AI Witchlabs, MediaTek, Huawei, Reality Labs, OPPO, Synaptics, Raspberry Pi, ETH Zürich (Computer Vision Lab) and University of Würzburg (Computer Vision Lab).

Author information

Authors and Affiliations

  1. Computer Vision Lab, ETH Zurich, Zurich, Switzerland

    Andrey Ignatov, Radu Timofte & Xin Lou

  2. AI Witchlabs, Zurich, Switzerland

    Andrey Ignatov & Radu Timofte

  3. University of Wuerzburg, Wuerzburg, Germany

    Radu Timofte, Marcos V. Conde & Ui-Jin Choi

  4. Xiaomi Inc., Beijing, China

    Shuai Liu, Chaoyu Feng, Furui Bai, Xiaotao Wang & Lei Lei

  5. Sanechips Co. Ltd., Shanghai, China

    Ziyao Yi, Yan Xiang, Zibin Liu, Shaoqing Li, Keming Shi, Dehui Kong & Ke Xu

  6. ENERZAi, Seoul, Korea

    Minsu Kwon

  7. Harbin Institute of Technology, Harbin, China

    Yaqi Wu & Feng Zhang

  8. Zhejiang University, Hangzhou, China

    Jiesi Zheng & Feng Zhang

  9. University of Shanghai for Science and Technology, Shanghai, China

    Zhihao Fan & Feng Zhang

  10. Tsinghua University, Beijing, China

    Xun Wu, Feng Zhang & Zhiming Wang

  11. Hongik University, Seoul, Korea

    Albert No & Minhyeok Cho

  12. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zewen Chen, Juan Wang, Haina Qin & Mingxuan Cai

  13. School of Computer Science, Fudan University, Shanghai, China

    Xiaze Zhang

  14. Washington University in St. Louis, Seattle, USA

    Ran Li

  15. Moscow Institute of Physics and Technology, Moscow, Russia

    Georgy Perevozchikov & Egor Ershov

  16. Alibaba DAMO Academy, Beijing, China

    Zheng Hui

  17. ShanghaiTech University, Shanghai, China

    Mengchuan Dong, Wei Zhou & Cong Pang

Authors
  1. Andrey Ignatov
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  2. Radu Timofte
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  3. Shuai Liu
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  4. Chaoyu Feng
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  5. Furui Bai
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  6. Xiaotao Wang
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  7. Lei Lei
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  8. Ziyao Yi
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  9. Yan Xiang
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  10. Zibin Liu
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  11. Shaoqing Li
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  12. Keming Shi
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  13. Dehui Kong
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  14. Ke Xu
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  15. Minsu Kwon
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  16. Yaqi Wu
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  17. Jiesi Zheng
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  18. Zhihao Fan
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  19. Xun Wu
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  20. Feng Zhang
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  21. Albert No
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  22. Minhyeok Cho
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  23. Zewen Chen
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  24. Xiaze Zhang
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  25. Ran Li
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  26. Juan Wang
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  27. Zhiming Wang
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  28. Marcos V. Conde
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  29. Ui-Jin Choi
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  30. Georgy Perevozchikov
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  31. Egor Ershov
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  32. Zheng Hui
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  33. Mengchuan Dong
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  34. Xin Lou
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  35. Wei Zhou
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  36. Cong Pang
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  37. Haina Qin
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  38. Mingxuan Cai
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Corresponding author

Correspondence to Andrey Ignatov .

Editor information

Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

A Teams and Affiliations

A Teams and Affiliations

1.1 Mobile AI 2022 Team

Title:

Mobile AI 2022 Learned Smartphone ISP Challenge

Members:

Andrey Ignatov\(^{1,2}\) (andrey@vision.ee.ethz.ch), Radu Timofte\(^{1,2,3}\)

Affiliations:

\(^1\) Computer Vision Lab, ETH Zurich, Switzerland

\(^2\) AI Witchlabs, Switzerland

\(^3\) University of Wuerzburg, Germany

1.2 MiAlgo

Title:

3Convs and BigUNet for Smartphone ISP

Members:

Shuai Liu (liushuai21@xiaomi.com), Chaoyu Feng, Furui Bai, Xiaotao Wang, Lei Lei

Affiliations:

Xiaomi Inc., China

1.3 Multimedia

Title:

FGARepNet: A real-time end-to-end ISP network based on Fine-Granularity attention and Re-parameter convolution

Members:

Ziyao Yi (yi.ziyao@sanechips.com.cn), Yan Xiang, Zibin Liu, Shaoqing Li, Keming Shi, Dehui Kong, Ke Xv

Affiliations:

Sanechips Co. Ltd, China

1.4 ENERZAi Research

Title:

Latency-Aware NAS and Histogram Feature Loss

Members:

Minsu Kwon (minsu.kwon@enerzai.com)

Affiliations:

ENERZAi, Seoul, Korea

enerzai.com

1.5 HITZST01

Title:

Residual Feature Distillation Channel Spatial Attention Network for ISP on Smartphones [71]

Members:

Yaqi Wu\(^1\) (titimasta@163.com), Jiesi Zheng\(^2\), Zhihao Fan\(^3\), Xun Wu\(^4\), Feng Zhang

Affiliations:

\(^1\) Harbin Institute of Technology, China

\(^2\) Zhejiang University, China

\(^3\) University of Shanghai for Science and Technology, China

\(^4\) Tsinghua University, China

1.6 MINCHO

Title:

Mobile-Smallnet: Smallnet with MobileNet blocks for an end-to-end ISP Pipeline

Members:

Albert No (albertno@hongik.ac.kr), Minhyeok Cho

Affiliations:

Hongik University, Korea

1.7 CASIA 1st

Title:

Learned Smartphone ISP Based On Distillation Acceleration

Members:

Zewen Chen\(^1\) (chenzewen2022@ia.ac.cn), Xiaze Zhang\(^2\), Ran Li\(^3\), Juan Wang\(^1\), Zhiming Wang\(^4\)

Affiliations:

\(^1\) Institute of Automation, Chinese Academy of Sciences, China

\(^2\) School of Computer Science, Fudan University, China

\(^3\) Washington University in St. Louis

\(^4\) Tsinghua University, China

1.8 JMU-CVLab

Title:

Shallow Non-linear CNNs as ISP

Members:

Marcos V. Conde (marcos.conde-osorio@uni-wuerzburg.de), Ui-Jin Choi

Affiliations:

University of Wuerzburg, Germany

1.9 DANN-ISP

Title:

Learning End-to-End Deep Learning Based Image Signal Processing Pipeline Using Adversarial Domain Adaptation

Members:

Georgy Perevozchikov (perevozchikov.gp@phystech.edu), Egor Ershov

Affiliations:

Moscow Institute of Physics and Technology, Russia

1.10 Rainbow

Title:

Auto White Balance UNet for Learned Smartphone ISP

Members:

Zheng Hui (huizheng.hz@alibaba-inc.com)

Affiliations:

Alibaba DAMO Academy, China

1.11 SKD-VSP

Title:

IFS Net-Image Frequency Separation Residual Network

Members:

Mengchuan Dong (mengchuan61@gmail.com), Wei Zhou, Cong Pang

Affiliations:

ShanghaiTech University, China

1.12 CHannel Team

Title:

GaUss-DWT net

Members:

Haina Qin (qinhaina2020@ia.ac.cn), Mingxuan Cai

Affiliations:

Institute of Automation, Chinese Academy of Sciences, China

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Ignatov, A. et al. (2023). Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13803. Springer, Cham. https://doi.org/10.1007/978-3-031-25066-8_3

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  • DOI: https://doi.org/10.1007/978-3-031-25066-8_3

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  • Online ISBN: 978-3-031-25066-8

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