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OISSR: Optical Image Stabilization Based Super Resolution on Smartphone Cameras

Published: 10 October 2022 Publication History

Abstract

Multi-frame super-resolution methods can generate high resolution images by combining multiple captures of the same scene; however, the performance of merged results are susceptible to degradation due to a lack of precision in image registration. In this study, we sought to develop a robust multi-frame super resolution method (called OISSR) for use on smartphone cameras with a optical image stabilizer (OIS). Acoustic injection is used to alter the readings from the built-in MEMS gyroscope to control the lens motion in the OIS module (note that the image sensor is fixed). We employ a priori knowledge of the induced lens motion to facilitate optical flow estimation with sub-pixel accuracy, and the output high-precision pixel alignment vectors are utilized to merge the multiple frames to reconstruct the final super resolution image. Extensive experiments on a OISSR prototype implemented on a Xiaomi 10Ultra demonstrate the high performance and effectiveness of the proposed system in obtaining the quadruple enhanced resolution imaging.

Supplementary Material

MP4 File (MM22-fp0891.mp4)
Presentation video of OISSR. In this video, we sought to develop a robust multi-frame super resolution method (called OISSR) for use on smartphone cameras with a optical image stabilizer (OIS). Acoustic injection is used to alter the readings from the built-in MEMS gyroscope to control the lens motion in the OIS module (note that the image sensor is fixed). We employ a priori knowledge of the induced lens motion to facilitate optical flow estimation with sub-pixel accuracy, and the output high-precision pixel alignment vectors are utilized to merge the multiple frames to reconstruct the final super resolution image. Extensive experiments on a OISSR prototype implemented on a Xiaomi 10Ultra demonstrate the high performance and effectiveness of the proposed system in obtaining the quadruple enhanced resolution imaging.

References

[1]
S Abhishek Anand and Nitesh Saxena. 2018. Speechless: Analyzing the threat to speech privacy from smartphone motion sensors. In 2018 IEEE Symposium on Security and Privacy (SP). IEEE, 1000--1017.
[2]
Simon Baker and Takeo Kanade. 2002. Limits on super-resolution and how to break them. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 9 (2002), 1167--1183.
[3]
Benedicte Bascle, Andrew Blake, and Andrew Zisserman. 1996. Motion deblurring and super-resolution from an image sequence. In European conference on computer vision. Springer, 571--582.
[4]
Goutam Bhat, Martin Danelljan, Luc Van Gool, and Radu Timofte. 2021. Deep burst super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9209--9218.
[5]
James C Brailean and Aggelos K Katsaggelos. 1995. Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences. IEEE Transactions on Image Processing 4, 9 (1995), 1236--1251.
[6]
David Capel and Andrew Zisserman. 2003. Computer vision applied to super resolution. IEEE Signal Processing Magazine 20, 3 (2003), 75--86.
[7]
Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. 2021. BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond. In Proceedings of the IEEE conference on computer vision and pattern recognition.
[8]
Michael M Chang, A Murat Tekalp, and M Ibrahim Sezan. 1997. Simultaneous motion estimation and segmentation. IEEE transactions on image processing 6, 9 (1997), 1326--1333.
[9]
Android Developers. 2022. OisSample. https://developer.android.com/reference/ android/hardware/camera2/params/OisSample. (2022).
[10]
P Erhan Eren, M Ibrahim Sezan, and A Murat Tekalp. 1997. Robust, objectbased high-resolution image reconstruction from low-resolution video. IEEE Transactions on Image Processing 6, 10 (1997), 1446--1451.
[11]
Ming Gao, Feng Lin, Weiye Xu, Muertikepu Nuermaimaiti, Jinsong Han, Wenyao Xu, and Kui Ren. 2020. Deaf-aid: mobile IoT communication exploiting stealthy speaker-to-gyroscope channel. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. 1--13.
[12]
Clément Godard, Kevin Matzen, and Matt Uyttendaele. 2018. Deep burst denoising. In Proceedings of the European Conference on Computer Vision (ECCV). 538--554.
[13]
Russell C Hardie, Kenneth J Barnard, and Ernest E Armstrong. 1997. Joint MAP registration and high-resolution image estimation using a sequence of undersampled images. IEEE transactions on Image Processing 6, 12 (1997), 1621-- 1633.
[14]
Russell C Hardie, Kenneth J Barnard, John G Bognar, Ernest E Armstrong, and Edward A Watson. 1998. High-resolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system. Optical Engineering 37, 1 (1998), 247--260.
[15]
Berthold KP Horn and Brian G Schunck. 1981. Determining optical flow. Artificial intelligence 17, 1--3 (1981), 185--203.
[16]
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. 2015. Single image superresolution from transformed self-exemplars. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5197--5206.
[17]
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox. 2017. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2462--2470.
[18]
Michal Irani and Shmuel Peleg. 1991. Improving resolution by image registration. CVGIP: Graphical models and image processing 53, 3 (1991), 231--239.
[19]
Camera Jabber. 2021. Which cameras have Pixel Shift? https://camerajabber. com/buyersguides/which-cameras-have-pixel-shift/. (2021).
[20]
kunzmi github. 2022. ImageStackAlignator: Implementation of Google's Handheld Multi-Frame Super-Resolution algorithm. https://github.com/kunzmi/ ImageStackAlignator. (2022).
[21]
Fabrizio La Rosa, Maria Celvisia Virzì, Filippo Bonaccorso, and Marco Branciforte. 2015. Optical Image Stabilization (OIS). STMicroelectronics. Available online: http://www. st. com/resource/en/white_paper/ois_white_paper. pdf (2015).
[22]
Wenbo Li, Xin Tao, Taian Guo, Lu Qi, Jiangbo Lu, and Jiaya Jia. 2020. Mucan: Multicorrespondence aggregation network for video super-resolution. In European Conference on Computer Vision. Springer, 335--351.
[23]
Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, and Jiaya Jia. 2020. Lapar: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond. Advances in Neural Information Processing Systems 33 (2020), 20343--20355.
[24]
Yinxiao Li, Pengchong Jin, Feng Yang, Ce Liu, Ming-Hsuan Yang, and Peyman Milanfar. 2021. COMISR: Compression-Informed Video Super-Resolution. In ICCV.
[25]
Bruce D Lucas, Takeo Kanade, et al. 1981. An iterative image registration technique with an application to stereo vision. Vancouver, British Columbia.
[26]
Andreas Lugmayr, Martin Danelljan, Luc Van Gool, and Radu Timofte. 2020. Srflow: Learning the super-resolution space with normalizing flow. In European conference on computer vision. Springer, 715--732.
[27]
Yan Michalevsky, Dan Boneh, and Gabi Nakibly. 2014. Gyrophone: Recognizing speech from gyroscope signals. In 23rd {USENIX} Security Symposium ({USENIX} Security 14). 1053--1067.
[28]
Philippos Mordohai. 2012. On the Evaluation of Scene Flow Estimation. In Computer Vision -- ECCV 2012. Workshops and Demonstrations. Springer Berlin Heidelberg, 148--157.
[29]
Karl S Ni and Truong Q Nguyen. 2007. Image superresolution using support vector regression. IEEE Transactions on Image Processing 16, 6 (2007), 1596--1610.
[30]
Hao Pan, Feitong Tan, Yi-Chao Chen, Gaoang Huang, Qingyang Li, Wenhao Li, Taoxue Guang, Lili Qiu, and Xiaoyu Ji. 2022. DoCam: Depth Sensing with an Optical Image Stabilization Supported RGB Camera. In Proceedings of the 28th Annual International Conference on Mobile Computing and Networking.
[31]
Shmuel Peleg, Danny Keren, and Limor Schweitzer. 1987. Improving image resolution using subpixel motion. Pattern recognition letters 5, 3 (1987), 223--226.
[32]
PetaPixel. 2015. A Practical Guide to Creating Superresolution Photos with Photoshop. https://petapixel.com/2015/02/21/a-practical-guide-to-creatingsuperresolution-photos-with-photoshop/. (2015).
[33]
Cory Rice. 2018. Pixel-Shift Shootout: Olympus vs. Pentax vs. Sony vs. Panasonic. https://www.bhphotovideo.com/explora/photography/tips-and-solutions/ pixel-shift-shootout-olympus-vs-pentax-vs-sony-vs-panasonic. (2018).
[34]
Dirk Robinson and Peyman Milanfar. 2004. Fundamental performance limits in image registration. IEEE Transactions on Image Processing 13, 9 (2004), 1185--1199.
[35]
Dirk Robinson and Peyman Milanfar. 2006. Statistical performance analysis of super-resolution. IEEE Transactions on Image Processing 15, 6 (2006), 1413--1428.
[36]
Yunmok Son, Hocheol Shin, Dongkwan Kim, Youngseok Park, Juhwan Noh, Kibum Choi, Jungwoo Choi, and Yongdae Kim. 2015. Rocking drones with intentional sound noise on gyroscopic sensors. In 24th {USENIX} Security Symposium ({USENIX} Security 15). 881--896.
[37]
Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. 2018. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In Proceedings of the IEEE conference on computer vision and pattern recognition. 8934--8943.
[38]
Yu-Wing Tai, Shuaicheng Liu, Michael S Brown, and Stephen Lin. 2010. Super resolution using edge prior and single image detail synthesis. In 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, 2400--2407.
[39]
Zachary Teed and Jia Deng. 2020. Raft: Recurrent all-pairs field transforms for optical flow. In European Conference on Computer Vision. Springer, 402--419.
[40]
William T Thomson. 2018. Theory of vibration with applications. CrC Press.
[41]
Timothy Trippel, Ofir Weisse, Wenyuan Xu, Peter Honeyman, and Kevin Fu. 2017. WALNUT: Waging doubt on the integrity of MEMS accelerometers with acoustic injection attacks. In 2017 IEEE European symposium on security and privacy (EuroS&P). IEEE, 3--18.
[42]
R Tsai. 1984. Multiframe image restoration and registration. Advance Computer Visual and Image Processing 1 (1984), 317--339.
[43]
Yazhou Tu, Zhiqiang Lin, Insup Lee, and Xiali Hei. 2018. Injected and delivered: Fabricating implicit control over actuation systems by spoofing inertial sensors. In 27th {USENIX} Security Symposium ({USENIX} Security 18). 1545--1562.
[44]
Michalis Vrigkas, Christophoros Nikou, and Lisimachos P Kondi. 2013. Accurate image registration for MAP image super-resolution. Signal Processing: Image Communication 28, 5 (2013), 494--508.
[45]
Philippe Weinzaepfel, Jerome Revaud, Zaid Harchaoui, and Cordelia Schmid. 2013. DeepFlow: Large displacement optical flow with deep matching. In Proceedings of the IEEE international conference on computer vision. 1385--1392.
[46]
Wikipedia. 2022. Wikipedia, Bicubic Interpolation. https://en.wikipedia.org/ wiki/Bicubic_interpolation. (2022).
[47]
Wikipedia. 2022. Wikipedia, Peak signal-to-noise ratio (PSNR). https://en. wikipedia.org/wiki/Peak_signal-to-noise_ratio. (2022).
[48]
Wikipedia. 2022. Wikipedia, Pinhole camera. https://en.wikipedia.org/wiki/ Pinhole_camera. (2022).
[49]
Wikipedia. 2022. Wikipedia, Structural similarity (SSIM). https://en.wikipedia. org/wiki/Structural_similarity. (2022).
[50]
Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. 2019. Handheld multi-frame super-resolution. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--18

Cited By

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  • (2024)M3Cam: Extreme Super-resolution via Multi-Modal Optical Flow for Mobile CamerasProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699371(744-756)Online publication date: 4-Nov-2024

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    cover image ACM Conferences
    MM '22: Proceedings of the 30th ACM International Conference on Multimedia
    October 2022
    7537 pages
    ISBN:9781450392037
    DOI:10.1145/3503161
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    Published: 10 October 2022

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    Author Tags

    1. image registration
    2. optical image stabilization
    3. super-resolution

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    • (2024)M3Cam: Extreme Super-resolution via Multi-Modal Optical Flow for Mobile CamerasProceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems10.1145/3666025.3699371(744-756)Online publication date: 4-Nov-2024

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