Google Pixel 4A Camera Noise Model - Revision history

imported>Student221: /* IMX363 Model and Results */

2020-11-27T19:40:05Z

IMX363 Model and Results

← Older revision		Revision as of 19:40, 27 November 2020
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	ISETCam's sensorSet() functions were used to create a model of the IMX363 imaging sensor analyzed in this project. Since our noise values were dependent on ISOSpeed, a model was created for each ISOSpeed measured. For each model, the overall values (not separated by color channel or location) for PRNU, DSNU, analog gain, dark current, and read noise were set as dictated by the results listed previously. Additional parameters corresponding to the Pixel 4A mechanical structure were set, including a focal length of 24mm, an f-number of 1.7, a pixel depth of 1.4 um, and a pixel array of 3024x4032.		ISETCam's sensorSet() functions were used to create a model of the IMX363 imaging sensor analyzed in this project. Since our noise values were dependent on ISOSpeed, a model was created for each ISOSpeed measured. For each model, the overall values (not separated by color channel or location) for PRNU, DSNU, analog gain, dark current, and read noise were set as dictated by the results listed previously. Additional parameters corresponding to the Pixel 4A mechanical structure were set, including a focal length of 24mm, an f-number of 1.7, a pixel depth of 1.4 um, and a pixel array of 3024x4032.

	Unfortunately, we did not have access to the raw sensor file, so we were unable to do a direct comparison between the model sensor data and actual sensor data. Instead, we compared the post-processed dataset images that we had with the model's image processing output, with the knowledge that this would introduce several uncertainties. For the comparison, an image of a uniform white field at an exposure time of 1.96ms was used at the various ISOSpeeds.		For the comparison, an image of a uniform white field at an exposure time of 1.96ms was used at the various ISOSpeeds. Only demosaicking was done in the image processing in order to provide the most accurate comparison between the model and actual data.

	The sensor model outputs and corresponding actual images are seen in Figures 18, 19, and 20, respectively. The leftmost images display the raw sensor output of the model (in terms of the RGB channels), the middle images display the model images after ~~post-processing~~, and the rightmost images display the corresponding images taken in a physical integrating sphere. ~~Unfortunately, since we do not know~~ the image ~~processing done in a Pixel 4A, it is impossible~~ to do a ~~direct comparison between~~ the ~~model and~~ actual ~~images. Additionally~~, the ~~actual images are JPEG files, meaning they have been compressed~~ and ~~have further artifacts, contributing even more~~ idealities~~. However, it does give~~ a ~~qualitative visual of what~~ the ~~actual camera produces~~, which ~~is beneficial for a first pass verification of~~ the model. ~~We can see that there may be errors in the analog gain, but this could also be due to the~~ color-correction in processing~~. Furthermore~~, ~~especially at~~ the ~~lower ISOSpeeds~~, the noise seems ~~to be~~ on par with ~~what~~ the ~~model produced, but this could be lost within the compression artifacts at higher ISOSpeeds~~.		The sensor model outputs and corresponding actual images are seen in Figures 18, 19, and 20, respectively. The leftmost images display the raw sensor output of the model (in terms of the RGB channels), the middle images display the model images after demosaicking, and the rightmost images display the corresponding images taken in a physical integrating sphere. A subset of the center of the images is displayed (rather than the whole image) to make the noise a bit more apparent. In the actual data, we see the effects of lens vignetting and non-idealities in the integrating sphere (present as a line across the image), which were not included in the model. No color-correction was included in the image processing, so the actual data appears more yellow than the modeled data From a qualitative perspective, the amount of noise in the model seems on par with that of the actual data.

	~~Although it is difficult to compare the final images, creating~~ the ~~model~~ helps in visualizing the noise produced at the pixel level, and how that translates into the final image through the model. When increasing ISOSpeed from 55 to 798, we can see a significant difference in the pixel values, which is represented by a substantial increase in image noise at a higher ISOSpeed.		The Bayer array on the left helps in visualizing the noise produced at the pixel level, and how that translates into the final image through the model. When increasing ISOSpeed from 55 to 798, we can see a significant difference in the pixel values in the model, which is represented by a substantial increase in image noise at a higher ISOSpeed.

	[[File:55_comp_new.png\| 1300x400px \| center \| thumb \| Fig. 17 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 55]]		[[File:55_comp_new.png\| 1300x400px \| center \| thumb \| Fig. 17 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 55]]

imported>Student221: /* IMX363 Model and Results */

2020-11-27T18:56:17Z

IMX363 Model and Results

← Older revision		Revision as of 18:56, 27 November 2020
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	Although it is difficult to compare the final images, creating the model helps in visualizing the noise produced at the pixel level, and how that translates into the final image through the model. When increasing ISOSpeed from 55 to 798, we can see a significant difference in the pixel values, which is represented by a substantial increase in image noise at a higher ISOSpeed.		Although it is difficult to compare the final images, creating the model helps in visualizing the noise produced at the pixel level, and how that translates into the final image through the model. When increasing ISOSpeed from 55 to 798, we can see a significant difference in the pixel values, which is represented by a substantial increase in image noise at a higher ISOSpeed.

	[[File:~~55_comp~~.png\| 1300x400px \| center \| thumb \| Fig. 17 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 55]]		[[File:55_comp_new.png\| 1300x400px \| center \| thumb \| Fig. 17 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 55]]

	[[File:~~198_comp~~.png\| 1300x400px \| center \| thumb \| Fig. 18 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 198]]		[[File:198_comp_new.png\| 1300x400px \| center \| thumb \| Fig. 18 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 198]]

	[[File:~~798_comp~~.png \| 1300x400px \| center \| thumb \| Fig. 19 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 798]]		[[File:798_comp_new.png \| 1300x400px \| center \| thumb \| Fig. 19 : Model sensor data (left), model image output (middle), and actual output (right) for ISOSpeed of 798]]

	== '''Conclusions''' ==		== '''Conclusions''' ==

imported>Student221: /* IMX363 Model and Results */

2020-11-27T18:07:32Z

IMX363 Model and Results

← Older revision		Revision as of 18:07, 27 November 2020
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	== '''IMX363 Model and Results''' ==		== '''IMX363 Model and Results''' ==

	ISETCam's sensorSet() functions were used to create a model of the IMX363 imaging sensor analyzed in this project. Since our noise values were dependent on ISOSpeed, a model was created for each ISOSpeed measured. For each model, the overall values (not separated by color channel or location) for PRNU, DSNU, analog gain, dark current, and read noise were set as dictated by the results listed previously. Additional parameters corresponding to the Pixel 4A mechanical structure were set, including a focal length of 24mm, an f-number of 1.7, a pixel ~~size~~ of 2.2 um, and a pixel array of 3024x4032.		ISETCam's sensorSet() functions were used to create a model of the IMX363 imaging sensor analyzed in this project. Since our noise values were dependent on ISOSpeed, a model was created for each ISOSpeed measured. For each model, the overall values (not separated by color channel or location) for PRNU, DSNU, analog gain, dark current, and read noise were set as dictated by the results listed previously. Additional parameters corresponding to the Pixel 4A mechanical structure were set, including a focal length of 24mm, an f-number of 1.7, a pixel depth of 1.4 um, and a pixel array of 3024x4032.

	Unfortunately, we did not have access to the raw sensor file, so we were unable to do a direct comparison between the model sensor data and actual sensor data. Instead, we compared the post-processed dataset images that we had with the model's image processing output, with the knowledge that this would introduce several uncertainties. For the comparison, an image of a uniform white field at an exposure time of 1.96ms was used at the various ISOSpeeds.		Unfortunately, we did not have access to the raw sensor file, so we were unable to do a direct comparison between the model sensor data and actual sensor data. Instead, we compared the post-processed dataset images that we had with the model's image processing output, with the knowledge that this would introduce several uncertainties. For the comparison, an image of a uniform white field at an exposure time of 1.96ms was used at the various ISOSpeeds.

imported>Student221: /* IMX363 Noise Characterization */

2020-11-27T18:06:13Z

IMX363 Noise Characterization

← Older revision		Revision as of 18:06, 27 November 2020
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	To isolate and measure the read noise of the sensor, all other sources of noise must be normalized, so that we can be sure any deviation in pixel value is due to the electronics, rather than any other source of fixed-pattern or temporal noise. Therefore, the images in the dataset were taken in the dark (to limit photon arrival and thus shot noise) and with extremely small exposure time (to limit dark current). Also, the average was taken for all of the images for a given ISOSpeed to normalize for any temporal noise. Therefore, any resulting variance in an image's pixel values are be primarily due to the read noise.		To isolate and measure the read noise of the sensor, all other sources of noise must be normalized, so that we can be sure any deviation in pixel value is due to the electronics, rather than any other source of fixed-pattern or temporal noise. Therefore, the images in the dataset were taken in the dark (to limit photon arrival and thus shot noise) and with extremely small exposure time (to limit dark current). Also, the average was taken for all of the images for a given ISOSpeed to normalize for any temporal noise. Therefore, any resulting variance in an image's pixel values are be primarily due to the read noise.

	The first analysis of the data was just comparing read noise at each ISOSpeed level, as shown in Figure 12. The read noise was calculated by taking the average standard deviation among all pixels in each image of a given ISOSpeed. The initial units of DN were converted to mV by multiplying by the full scale range of the sensor over the number of quantization levels. These initial results show a clear impact of ISOSpeed on the read noise, with higher ISOSpeeds resulting in higher read noise measurements. Although more data at various ISOSpeeds is needed to fully deduce the relationship, it ~~does not appear~~ to be linear. The benefits of greater sensitivity given by using a large ISOSpeed are consequently accompanied by a heavy cost in terms of noise. This relationship can qualitatively be seen in Figure 13 [8].		The first analysis of the data was just comparing read noise at each ISOSpeed level, as shown in Figure 12. The read noise was calculated by taking the average standard deviation among all pixels in each image of a given ISOSpeed. The initial units of DN were converted to mV by multiplying by the full scale range of the sensor over the number of quantization levels. These initial results show a clear impact of ISOSpeed on the read noise, with higher ISOSpeeds resulting in higher read noise measurements. Although more data at various ISOSpeeds is needed to fully deduce the relationship, it appears to be a strong positive linear relationship. The benefits of greater sensitivity given by using a large ISOSpeed are consequently accompanied by a heavy cost in terms of noise. This relationship can qualitatively be seen in Figure 13 [8].

	[[File:~~Read_iso~~.PNG \| ~~1211x500px~~ \| center \| thumb \| Fig. 14 : Read Noise versus ISOSpeed]]		[[File:Read_iso_line.PNG \| 666x381px \| center \| thumb \| Fig. 14 : Read Noise versus ISOSpeed]]

	[[File:Iso_images.PNG\| 1125x500px \| center \| thumb \| Fig. 15 : Greater ISOSpeeds result in greater noise]]		[[File:Iso_images.PNG\| 1125x500px \| center \| thumb \| Fig. 15 : Greater ISOSpeeds result in greater noise]]

imported>Student221: /* Noise Sources and the Readout Circuit Model */

2020-11-19T23:14:33Z

Noise Sources and the Readout Circuit Model

← Older revision		Revision as of 23:14, 19 November 2020
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	Other sources of characterized noise that play a critical role in the performance of the CMOS image sensor is the Dark Current Rate and the Read Noise [3]. Referring the Fig. 1, the Dark Current Rate is the mean gain between the pixels without an illuminant caused by leakage current inherent to the electronics that is indistinguishable from external photons. The largest culprit of such is the reverse-biased diode in the standard 4T-pixel array. This leakage is mostly process dependent and is attributed to interface and material defects in the semiconductor. Read Noise, however, is largely caused from the sampling of the pinned-photodiode in the 4T-pixel and the resulting quantization of the output voltage from the Analog-to-Digital converter at the output of the readout circuitry.		Other sources of characterized noise that play a critical role in the performance of the CMOS image sensor is the Dark Current Rate and the Read Noise [3]. Referring the Fig. 1, the Dark Current Rate is the mean gain between the pixels without an illuminant caused by leakage current inherent to the electronics that is indistinguishable from external photons. The largest culprit of such is the reverse-biased diode in the standard 4T-pixel array. This leakage is mostly process dependent and is attributed to interface and material defects in the semiconductor. Read Noise, however, is largely caused from the sampling of the pinned-photodiode in the 4T-pixel and the resulting quantization of the output voltage from the Analog-to-Digital converter at the output of the readout circuitry.

	'''~~Redadout~~ Noise'''		'''Readout Noise'''

	Referring the Fig. 2, one can see the typical model of the Readout Circuit chain. Starting from the left, we can see the model of the 4T-Pixel cell. It consists of the photo-element, a sampling capacitor, and a readout source follower. Common sources of noise in this cell consist of the Shot Noise from the pinned-photodiode, Thermal and Flicker Noise from the MOS Source Follower, and gain mismatch in the Source Follower stage dependent on the Threshold Voltage of the MOS device. The next cell, the Programmable Gain Amplifier (PGA) introduces noise in the form of Thermal and Flicker Noise, and much like the Source Follower stage, gain variation dependent on device mismatch. The Sample-and-Hold cell that follows contributes kTC noise largely mitigated through the use of a technique known as Correlated Double Sampling. Lastly, we find ourselves at the Analog-Digital Converter, which offer its own noise source in the form of Quantization Noise and variations in its performance (INL/DNL) due to device mismatch.		Referring the Fig. 2, one can see the typical model of the Readout Circuit chain. Starting from the left, we can see the model of the 4T-Pixel cell. It consists of the photo-element, a sampling capacitor, and a readout source follower. Common sources of noise in this cell consist of the Shot Noise from the pinned-photodiode, Thermal and Flicker Noise from the MOS Source Follower, and gain mismatch in the Source Follower stage dependent on the Threshold Voltage of the MOS device. The next cell, the Programmable Gain Amplifier (PGA) introduces noise in the form of Thermal and Flicker Noise, and much like the Source Follower stage, gain variation dependent on device mismatch. The Sample-and-Hold cell that follows contributes kTC noise largely mitigated through the use of a technique known as Correlated Double Sampling. Lastly, we find ourselves at the Analog-Digital Converter, which offer its own noise source in the form of Quantization Noise and variations in its performance (INL/DNL) due to device mismatch.

imported>Student221: /* Conclusions */

2020-11-19T03:59:04Z

Conclusions

← Older revision		Revision as of 03:59, 19 November 2020
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	== '''Conclusions''' ==		== '''Conclusions''' ==
			Through this project, we were able to accomplish our goal of measuring PRNU, DSNU, dark current, and read noise. We compared noise of the overall image to its composite color channels, as well as to different locations of the pixel array to account for lens vignetting and column ADCs, and showed the impact that each of these factors had. Finally, we studied how all of these metrics varied according to ISOSpeed. Using the ISETcam model, we were able to create a representative pixel array, and the corresponding output image, at each ISOSpeed. This showed the significant impact that ISOSpeed has on the resulting image, and how that noise is introduced at the pixel level. For a better comparison between the model and actual datasets, access to the raw sensor output (rather than just the DNG or JPEG), is necessary. With that information, we anticipate that we would be able to isolate errors in our created model and ideally have a better representation of the Pixel 4A camera and sensor.

	== '''References''' ==		== '''References''' ==

imported>Student221: /* Appendix */

2020-11-18T23:31:57Z

Appendix

← Older revision		Revision as of 23:31, 18 November 2020
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	- "GenerateDataStructures_**.m" : Will generate structures for all of the datasets (PRNU/DSNU, DCR, ReadNoise) either at the image or pixel level.		- "GenerateDataStructures_**.m" : Will generate structures for all of the datasets (PRNU/DSNU, DCR, ReadNoise) either at the image or pixel level.

			- "ReadNoise_Analysis.m" : Reads in ReadNoise output data structure and computes/plots results.


			'''Work Breakdown :'''

			Joey: Created scripts for generating structures from the datasets. Did analysis for PRNU/DSNU and dark current.

			Melissa: Did analysis for read noise. Created script for the sensor model.

imported>Student221: /* IMX363 Noise Characterization */

2020-11-18T23:26:18Z

IMX363 Noise Characterization

← Older revision		Revision as of 23:26, 18 November 2020
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	To isolate and measure the read noise of the sensor, all other sources of noise must be normalized, so that we can be sure any deviation in pixel value is due to the electronics, rather than any other source of fixed-pattern or temporal noise. Therefore, the images in the dataset were taken in the dark (to limit photon arrival and thus shot noise) and with extremely small exposure time (to limit dark current). Also, the average was taken for all of the images for a given ISOSpeed to normalize for any temporal noise. Therefore, any resulting variance in an image's pixel values are be primarily due to the read noise.		To isolate and measure the read noise of the sensor, all other sources of noise must be normalized, so that we can be sure any deviation in pixel value is due to the electronics, rather than any other source of fixed-pattern or temporal noise. Therefore, the images in the dataset were taken in the dark (to limit photon arrival and thus shot noise) and with extremely small exposure time (to limit dark current). Also, the average was taken for all of the images for a given ISOSpeed to normalize for any temporal noise. Therefore, any resulting variance in an image's pixel values are be primarily due to the read noise.

	The first analysis of the data was just comparing read noise at each ISOSpeed level, as shown in Figure 12. The read noise was calculated by taking the average standard deviation among all pixels in each image of a given ISOSpeed. The initial units of DN were converted to mV by multiplying by the full scale range of the sensor over the number of quantization levels. These initial results show a clear impact of ISOSpeed on the read noise, with higher ISOSpeeds resulting in higher read noise measurements. Although more data at various ISOSpeeds is needed to fully deduce the relationship, it does not appear to be linear. The benefits of greater sensitivity given by using a large ISOSpeed are consequently accompanied by a heavy cost in terms of noise. This relationship can qualitatively be seen in Figure 13 [~~REF - http://davemorrisonphotos.com/photo-tutorials/iso-sensitivity-light~~].		The first analysis of the data was just comparing read noise at each ISOSpeed level, as shown in Figure 12. The read noise was calculated by taking the average standard deviation among all pixels in each image of a given ISOSpeed. The initial units of DN were converted to mV by multiplying by the full scale range of the sensor over the number of quantization levels. These initial results show a clear impact of ISOSpeed on the read noise, with higher ISOSpeeds resulting in higher read noise measurements. Although more data at various ISOSpeeds is needed to fully deduce the relationship, it does not appear to be linear. The benefits of greater sensitivity given by using a large ISOSpeed are consequently accompanied by a heavy cost in terms of noise. This relationship can qualitatively be seen in Figure 13 [8].

	[[File:Read_iso.PNG \| 1211x500px \| center \| thumb \| Fig. 14 : Read Noise versus ISOSpeed]]		[[File:Read_iso.PNG \| 1211x500px \| center \| thumb \| Fig. 14 : Read Noise versus ISOSpeed]]

imported>Student221: /* Noise Sources and the Readout Circuit Model */

2020-11-18T23:25:56Z

Noise Sources and the Readout Circuit Model

← Older revision		Revision as of 23:25, 18 November 2020
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	[[File:Readout_Noise_Model_v2.png \| 789x262px \| center \| thumb \| Fig. 2 : Complete Readout Model]]		[[File:Readout_Noise_Model_v2.png \| 789x262px \| center \| thumb \| Fig. 2 : Complete Readout Model]]

	In a CMOS sensor, each of these pixels and its corresponding readout electronics are laid out in a grid. In a typical sensor, each pixel has its own amplifier with associated gain and offset noise. Assuming an operational amplifier model, the distribution of this error should approach a Gaussian across all of the pixels. Additionally, each column of pixels is converted from the analog digital domains (via ADCs), which can also have its own amplifier. A typical pixel array is shown in Fig. 3 [~~INSERT REF - https://andor.oxinst.com/learning/view/article/understanding-read-noise-in-scmos-cameras~~]. These readout electronics cause an additional gain/offset error due to the amplifiers and quantization error introduced by the ADCs that vary on a column-by-column basis. Such noise also approaches a Gaussian distribution. Therefore, the sum of all of these readout electronics introduce noise both on a pixel and a column basis.		In a CMOS sensor, each of these pixels and its corresponding readout electronics are laid out in a grid. In a typical sensor, each pixel has its own amplifier with associated gain and offset noise. Assuming an operational amplifier model, the distribution of this error should approach a Gaussian across all of the pixels. Additionally, each column of pixels is converted from the analog digital domains (via ADCs), which can also have its own amplifier. A typical pixel array is shown in Fig. 3 [3]. These readout electronics cause an additional gain/offset error due to the amplifiers and quantization error introduced by the ADCs that vary on a column-by-column basis. Such noise also approaches a Gaussian distribution. Therefore, the sum of all of these readout electronics introduce noise both on a pixel and a column basis.

	[[File:readout_schem.jpg \| 789x262px \| center \| thumb \| Fig. 3 : CMOS Pixel Array]]		[[File:readout_schem.jpg \| 789x262px \| center \| thumb \| Fig. 3 : CMOS Pixel Array]]

imported>Student221: /* References */

2020-11-18T23:25:24Z

References

← Older revision		Revision as of 23:25, 18 November 2020
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	[7] A simulation tool for evaluating digital camera image quality (2004). J. E. Farrell, F. Xiao, P. Catrysse, B. Wandell . Proc. SPIE vol. 5294, p. 124-131, Image Quality and System Performance, Miyake and Rasmussen (Eds). January 2004		[7] A simulation tool for evaluating digital camera image quality (2004). J. E. Farrell, F. Xiao, P. Catrysse, B. Wandell . Proc. SPIE vol. 5294, p. 124-131, Image Quality and System Performance, Miyake and Rasmussen (Eds). January 2004

			[8] "What is ISO? Your Camera's Sensitivity to Light Explained," [Online]. Available: http://davemorrisonphotos.com/photo-tutorials/iso-sensitivity-light.

	== '''Appendix'''==		== '''Appendix'''==