Photographic Noise Performance Measures Based on RAW Files Analysis of Consumer Cameras

[EN] Photography is being benefited from the huge improvement in CMOS image sensors. New cameras extend the dynamic range allowing photographers to take photos with a higher quality than they could imagine one decade ago. However, the existence of different technologies make more complicated the photographic analysis of how to determine the optimal camera exposure settings. In this paper, we analyze how the different noise models are translated to different signal to noise SNR curve patterns and which factors are relevant. In particular, we discuss profoundly the relationships between exposure settings (shutter speed, aperture and ISO). Since a fair comparison between cameras can be tricky because of different pixel size, sensor format or ISO scale definition, we explain how the pixel analysis of a camera can be translated to a more helpful universal photographic noise measure based on human perception and common photography rules. We analyze the RAW files of different camera models and show how the noise performance analysis (SNR and dynamic range) interact with photographer's requirements.Igual García, J. (2019). Photographic Noise Performance Measures Based on RAW Files Analysis of Consumer Cameras. Electronics. 8(11):1-30. https://doi.org/10.3390/electronics8111284S130811Camera Imaging Products Association: Digital Cameras Reporthttp://cipa.jp/stats/dc_e.htmlGye, L. (2007). Picture This: the Impact of Mobile Camera Phones on Personal Photographic Practices. Continuum, 21(2), 279-288. doi:10.1080/10304310701269107Bhandari, A., & Raskar, R. (2016). Signal Processing for Time-of-Flight Imaging Sensors: An introduction to inverse problems in computational 3-D imaging. IEEE Signal Processing Magazine, 33(5), 45-58. doi:10.1109/msp.2016.2582218Wang, J., Zhang, C., & Hao, P. (2011). New color filter arrays of high light sensitivity and high demosaicking performance. 2011 18th IEEE International Conference on Image Processing. doi:10.1109/icip.2011.6116336Chan, C.-C., & Chen, H. H. (2018). Improving the Reliability of Phase Detection Autofocus. Electronic Imaging, 2018(5), 241-1-241-5. doi:10.2352/issn.2470-1173.2018.05.pmii-241Kirkpatrick, K. (2019). The edge of computational photography. Communications of the ACM, 62(7), 14-16. doi:10.1145/3329721Koppal, S. J. (2016). A Survey of Computational Photography in the Small: Creating intelligent cameras for the next wave of miniature devices. IEEE Signal Processing Magazine, 33(5), 16-22. doi:10.1109/msp.2016.2581418CMOS Image Sensor Market: Forecasts from 2019 to 2024https://www.knowledge-sourcing.com/report/cmos-Image-sensor-marketPhotonstophotos.nethttp://photonstophotos.netDxomarkhttp://dxomark.comBoukhayma, A., Peizerat, A., & Enz, C. (2016). Temporal Readout Noise Analysis and Reduction Techniques for Low-Light CMOS Image Sensors. IEEE Transactions on Electron Devices, 63(1), 72-78. doi:10.1109/ted.2015.2434799Vargas-Sierra, S., Linán-Cembrano, G., & Rodríguez-Vázquez, A. (2015). A 151 dB High Dynamic Range CMOS Image Sensor Chip Architecture With Tone Mapping Compression Embedded In-Pixel. IEEE Sensors Journal, 15(1), 180-195. doi:10.1109/jsen.2014.2340875Hassan, N. B., Huang, Y., Shou, Z., Ghassemlooy, Z., Sturniolo, A., Zvanovec, S., … Le-Minh, H. (2018). Impact of Camera Lens Aperture and the Light Source Size on Optical Camera Communications. 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP). doi:10.1109/csndsp.2018.8471766Hirsch, J., & Curcio, C. A. (1989). The spatial resolution capacity of human foveal retina. Vision Research, 29(9), 1095-1101. doi:10.1016/0042-6989(89)90058-8ColorChecker Classic Charthttps://xritephoto.com/colorchecker-classicWang, F., & Theuwissen, A. (2017). Linearity analysis of a CMOS image sensor. Electronic Imaging, 2017(11), 84-90. doi:10.2352/issn.2470-1173.2017.11.imse-191Wakashima, S., Kusuhara, F., Kuroda, R., & Sugawa, S. (2015). Analysis of pixel gain and linearity of CMOS image sensor using floating capacitor load readout operation. Image Sensors and Imaging Systems 2015. doi:10.1117/12.2083111Wang, F., Han, L., & Theuwissen, A. J. P. (2018). Development and Evaluation of a Highly Linear CMOS Image Sensor With a Digitally Assisted Linearity Calibration. IEEE Journal of Solid-State Circuits, 53(10), 2970-2981. doi:10.1109/jssc.2018.285625

CMOS Sensors for Precision Astronomy

Thanks to rapid development in recent years, CMOS sensors are quickly approaching the performance levels achieved by scientific CCDs. They are now at a point where they are being considered for precision astronomy measurements, such as gravitational microlensing. CMOS sensors offer a number of inherent advantages over CCDs, such as higher frame rate and radiation hardness. In this work, a novel monolithic CMOS sensor capable of full depletion through an applied reverse bias is characterised and tested. Baseline characterisation of basic metrics is undertaken to ensure the device is fully operational. Image lag is measured in the device to determine optimal operating parameters. A novel method for reducing image lag is described and tested, with results indicating a successful reduction. Inter-pixel non-uniformity is investigated to examine the different photo-response from pixel to pixel, as well as intra-pixel non-uniformity to determine the areas of a pixel which are more sensitive to incoming photons than others. The point spread function of the sensor is then tested at multiple reverse biases to ensure that full depletion has been achieved and compared to results taken from a CCD