Abstract
In this paper, we introduce the challenges, intrinsic and extrinsic, of color and depth sensors integration in the same matrix for a monolithic RBG-Z CMOS imager system. Due to the fact that the technology to conceive this type of circuit is still under development, the challenge that we address is the extrinsic one. It is a consequence of the heterogeneity of the matrix, where information is missing compared to what can be provided by separate RGB and Z systems. For that a first evaluation is done taking into account how the RGB-Z patterns could impact the demosaicing step. The evaluated pattern are in function of the different sizes between color and depth pixels. For the missing color reconstruction we have evaluated the state of the art algorithms, adapted to the missing information, and we propose an original adaptive algorithm using a new operator called semi-gradient (SG). To fill the lack of a mature technology for which real images are missing for this type of CMOS imager, a test environment was created and then used with three different databases, Kodak, McMaster, HDR+burst. The results show improvements on edges, corners, and narrow lines reconstruction, and a reduction of color and structural artefacts compared to the state-of-the-art reconstruction algorithms.
Similar content being viewed by others
Notes
Internet of Things
Color Depth Filter Array
Edge Directed Interpolation
Near InfraRed
Peak signal-to-noise-ratio
Color Peak signal-to-noise-ratio
Structural Similarity index
Multi scale Structural Similarity index
stands for International Commission on Illumination. It is the international authority on light, illumination, color, and color spaces
References
Alper, G. (2015). 1000 Frames per second cameras: fast frame rates with ultra high resolution industrial cameras. https://www.adimec.com/1000-frames-per-second-cameras-fast-frame-rates-with-ultra-high-resolution-industrial-cameras/. Accessed 20 Dec. 2021.
Gamal, A. E. (2002). High Dynamic Range Image Sensors. International Solid-State Circuits Conference, 290.
Bevilacqua, A., Di Stefano, L., & Azzari, P. (2006). People tracking using a time-of-flight depth sensor. Proceedings - IEEE International Conference on Video and Signal Based Surveillance. https://doi.org/10.1109/AVSS.2006.92
Microsoft: Microsoft Kinect. https://en.wikipedia.org/wiki/Kinect. Accessed 20 Dec. 2021.
Litomisky, K. (2012). Consumer RGB-d cameras and their applications. Tech. rep. http://alumni.cs.ucr.edu/~klitomis/files/RGBD-intro.pdf. Accessed 20 Dec. 2021.
Tillman, M. (2020). What is Apple Face ID and how does it work? https://www.pocket-lint.com/phones/news/apple/142207-what-is-apple-face-id-and-how-does-it-work. Accessed 20 Dec. 2021.
Gove, R. J. (2020). CMOS image sensor technology advances for mobile devices. In: High Performance Silicon Imaging. Elsevier Ltd., (2nd ed., pp. 185–240). https://doi.org/10.1016/B978-0-08-102434-8.00007-6. http://dx.doi.org/10.1016/B978-0-08-102434-8.00007-6
Kim, W., Yibing, W., Ovsiannikov, I., Lee, S., Park, Y., Chung, C., & Fossum, E. (2012). A 1.5Mpixel RGBZ CMOS image sensor for simultaneous color and range image capture. Digest of Technical Papers - IEEE International Solid-State Circuits Conference, 55, 392–393. https://doi.org/10.1109/ISSCC.2012.6177061
Rodrigues, B., Guillon, M., Billon-Pierron, N., Mancini, J. B., Giffard, B., Cazaux, Y., Malinge, P., Waltz, P., Ngoua, A., Taluy, A., Kuster, S., Joblot, S., Roy, F., Lu, G. N., Lyon, N. D., & Lyon, B. (2017). Indirect ToF Pixel integrating fast buried-channel transfer gates and gradual epitaxy, and enabling CDS. International Image Sensor Workshop, 266–269.
Monno, Y., Teranaka, H., Yoshizaki, K., Tanaka, M., & Okutomi, M. (2019). Single-sensor RGB-NIR imaging: High-quality system design and prototype implementation. IEEE Sensors Journal, 19(2), 497–507. https://doi.org/10.1109/JSEN.2018.2876774
Teranaka, H., Monno, Y., Tanaka, M., & Ok, M. (2016). Single-sensor RGB and NIR image acquisition: toward optimal performance by taking account of CFA Pattern, demosaicking, and color correction. Electronic Imaging, 2016(18), 1–6. https://doi.org/10.2352/ISSN.2470-1173.2016.18.DPMI-256
Navinprashath, R. R., & Radhesh, B. (2019). Learning based demosaicing and color correction for RGB-IR patterned image sensors. Society for Imaging Science and Technology, 1–6. https://doi.org/10.2352/ISSN.2470-1173.2019.15.AVM-045
Koyama, S., Inaba, Y., Kasano, M., & Murata, T. (2008). A day and night vision MOS Imager with robust. 55(3), 754–759.
Shi, L., Ovsiannikov, I., Min, D. K., Noh, Y., Kim, W., & Jung, S. (2013). Demosaicing for RGBZ Sensor. SPIE Proceedings, 8657, 1–9. https://doi.org/10.1117/12.2001702
Shi, L., & Ovsiannikov, I. (2015). Demosaicing for RGBZ sensor (Nb US 2015/0022869 A1).
Ramanath, R., & Snyder, W. E. (2003). Adaptive demosaicking. Journal of Electronic Imaging, 12, 633–642. https://doi.org/10.1117/1.1606459
Malvar, H., Li-Wei He, & Cutler, R. (2004). High-quality linear interpolation for demosaicing of Bayer-patterned color images. 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, III–485–8. https://doi.org/10.1109/ICASSP.2004.1326587. http://ieeexplore.ieee.org/document/1326587/
Rebiere, V., Drouot, A., Granado, B., Bourge, A., & Pinna, A. (2020). Semi-Gradient for Color Pixel Reconstruction in a RGBZ CMOS Sensor. 2020 IEEE Sensors, 6–9. https://doi.org/10.1109/SENSORS47125.2020.9278887
Breitbarth, A., Schardt, T., Kind, C., Brinkmann, J., Dittrich, P. G., & Notni, G. (2019). Measurement accuracy and dependence on external influences of the iPhone X TrueDepth sensor (October), 7. https://doi.org/10.1117/12.2530544
Alacoque, L. (2015). Method for demosaicing a raw digital image, corresponding computer program and imaging or graphic circuit (number EP 2426639 B1).
Tomasi, C., & Manduchi, R. (1998). Bilateral Filtering for Gray and Color Images. International Conference on Computer Vision.
Horé, A., & Ziou, D. (2011). An edge-sensing generic demosaicing algorithm with application to image resampling. IEEE Transactions on Image Processing, 20(11), 3136–3150. https://doi.org/10.1109/TIP.2011.2159229
Adlakha, D., Adlakha, D., & Tanwar, R. (2016). Analytical Comparison between Sobel and Prewitt Edge Detection Techniques. 7(1), 1482–1485.
Alakarhu, J. (2007). Image Sensors and Image Quality in Mobile Phones. International Image Sensor Workshop, Ogunquit (pp. 7–18).
Kodak color image dataset. http://r0k.us/graphics/kodak/. Accessed 20 Dec. 2021.
Zhang, L., Wu, X., Buades, A., & Li, X. (2011). Color Demosaicking by Local Directional Interpolation and Nonlocal Adaptive Thresholding. Electronic Imaging, 1–29.
Hasinoff, S. W., Sharlet, D. Geiss, R., Levoy, A. A., Barron, J. T., Kainz, F., Chen, J., & L., M. (2016). Burst photography for high dynamic range and low-light imaging on mobile cameras. ACM Transactions on Graphics, 35. http://hdrplusdata.org/dataset.html. Accessed 20 Dec. 2021.
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simmoncelli, E. P. (2004). Image quality assessment: form error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. https://doi.org/10.1109/TIP.2003.819861
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003). Multi-scale Structural Similarity For image Quality Assessment. IEEE Asilomar Conference on Signals, Systems and Computer, 2, 9–13.
Lu, W., & Tan, Y. P. (2003). Color filter array demosaicking: new method and performance measures. IEEE Transactions on Image Processing : a publication of the IEEE Signal Processing Society, 12(10), 1194–1210. https://doi.org/10.1109/TIP.2003.816004
Fairchild, M. D. (1997). Color Appearance Models.
Mahy, M., Eycken, L. V., & Oosterlinck, A. (1994). Evaluation of Uniform Color Spaces Developed after the Adoption of CIELAB and CIELUV. Color Research and Application, 19(2), 105–121.
Yasuma, F., Mitsunaga, T., Iso, D., & Nayar, S. K. (2008). Generalized Assorted Pixel Camera: Post-Capture Control of Resolution, Dynamic Range and Spectrum. Tech. rep.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rebiere, V., Drouot, A., Granado, B. et al. Color Pixel Reconstruction for a Monolithic RGB-Z CMOS Imager. J Sign Process Syst 94, 623–644 (2022). https://doi.org/10.1007/s11265-021-01726-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-021-01726-3