WeiHsuChaoTsunHanHuang
Introduction
Sensor noise analysis is one of the most important features that we need to take care of when we are testing our sensors. Today, the CMOS image sensor is widely used by many smart phones. There are some common camera (sensor) noise that exist in such type of image sensor. One is fixed pattern noise (FPN) and the other one is temporal noise. For the former one, there are two major noise we have to further explore. One is PRNU (Photo-Response Non-Uniformity) and the other one is DSNU (Dark Signal Non-Uniformity). For the temporal noise, read noise and dark noise are the two we are going to measure in this project. This project is going to give a high-level view of how to calculate and measure these values with different color channels and ISO speed. We aim to find some relationship or pattern during this project.
Background
Noise Source
Fixed Pattern Noise The fixed pattern noise can be referred to a particular noise pattern that we can measure it from a particular sensor during exposure of light. It can vary with different conditions under which sensors are. For example, environment temperature, image integration time and exposure time can affect fixed pattern noise. PRNU and DSNU are fixed pattern noise.
DSNU
PRNU
Temporal Noise
Read noise
Dark noise (dark current rate)
Methods
DSNU and PRNU
Step 1: read in multiple dng files with different time frames
Step 2: crop the original image with a particular location and select a particular channel
Step 3: calculate the variance for the slope and offset after fitting the straight line to the plot (pixel value and exposure time)
Step 4: do the unit conversion to voltage
Read Noise and Dark Noise
Step 1: read in multiple dng files with different time frames
Step 2: crop the original image with a particular location and select a particular channel
Step 3: calculate the variance for the slope and offset after fitting the straight line to the plot (pixel value and exposure time)
Step 4: do the unit conversion to voltage
Results
1. PRNU Result
a. Comparison of different color channels
Blue Channel has the highest PRNU percentage, while Red Channel and Green have similar PRNU percentages. (usually Blue > Red > Green)
b. Comparison of different position
(1,1) square has higher PRNU percentage than (2001, 1501) square.
c. Comparison of different ISO Speed
Higher ISO Speed leads to larger PRNU percentage.
2. DSNU Result
a. Comparison of different color channels
Green Channel has the highest DSNU, while Blue Channel has the lowest DSNU. (Green > Red > Blue)
b. Comparison of different position
(2001,1501) square has higher DSNU than (1, 1) square.
c. Comparison of different ISO Speed
Higher ISO Speed leads to larger DSNU value.
3. PRNU and DSNU Distribution
Because we are experimenting on the noise model of pixel level noise (Photon Response and Dark Signal, PR/DS), it is interesting to see the distributions after we have collected the PR/DS values of a larger number of pixels (100 * 100 = 10000). We expect that the distributions are almost bell curves and approach Gaussian Distribution with a large number of values.
Fixing ISO Speed to 299, color in green, and location at (1,1) square, we can see that the distribution of Photon Response and that of Dark Signal are similar to Gaussian Distribution :
4. Dark Noise Result
a. Comparison of different color channels
For A2 Block (Orange bars), green channel has highest value at ISO 55 and ISO 99, but Green channel has lowest value at ISO 299. So, which color a channel belongs to does not affect the magnitude of the pixel's dark noise.
b. Comparison of different position
Fixed ISO 55, at the red channel, the dark current rate at (1, 1) square is higher than that at (2001, 1501) square. However, at the green channel, the dark current rate at (1, 1) square is much lower than that at (2001, 1501) square. So, position on the image does not affect the magnitude of dark noises.
c. Comparison of different ISO Speed
Looking into the values, we can observe that higher ISO Speed will lead to larger dark noise voltages per second after applying analog gain.
5. Read Noise Result
a. Comparison of different color channels
The read noises at red channel are similar to those at blue channel. The read noises at green channel are higher than those at red and blue ones.
b. Comparison of different position
The read noises at (1,1) square are similar to the read noises at (2001,1501) square. Position on the image does not affect the magnitude of read noises.
c. Comparison of different ISO Speed
We can see that higher ISO Speed will cause larger read noise voltages after applying analog gain.
Conclusions
Appendix
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