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

Appendix

You can write math equations as follows: $y=x+5$

You can include images as follows (you will need to upload the image first using the toolbox on the left bar, using the "Upload file" link).