CMOS image sensors are becoming more and more complicated. In the mid-nineties the devices were simple image sensors, but over the recent years they have become complete camera systems.

Characterization and evaluation of these highly sophisticated SoC's (system-on-chip) is no longer straightforward.

Furthermore the pixels of the sensors are becoming extremely small and their limited size can have negative effects on dynamic range, light sensitivity, noise and speed.

In the context of further optimization of the imaging functionality, it is of great importance to have a good understanding of performance-limiting parameters of the system. These can only be revealed by performing dedicated measurements on the image sensors and/or on the complete camera systems.

solid-state image sensors and digital cameras
solid-state image sensors and digital cameras


It may sound strange that an image sensor, which is made to capture light, will be characterized first in dark conditions. But actually this should not really be surprising because noise will first become visible in the darkest parts of an image. For that reason the dark performance of an image sensor plays crucial role. It also sets the lower end of the dynamic range.

In this course, the fixed-pattern noise (= correlated noise) will be measured first, next the temporal noise (= uncorrelated noise) will be characterized. All measurements will be done based on real images taken by a commercially available camera. For both noise types, some extra statistical operations will allow to split the overall noise characterized into a contribution on row level, column level and pixel level. This gives very useful information on where to find the root cause of the noise issues. The overall measurement process will involve grabbing a particular number of dark images at various exposure times. Some simple statistical operations will split the noise into its various components.

At the end of the measurement cycle of noise in dark, the following parameters can be retrieved : offset of the sensor, noise floor in dark, conversion gain, dark current, dark signal-non-uniformity, total fixed-pattern noise, row level fixed-pattern noise, column-level fixed-pattern noise, pixel-level fixed pattern noise, temporal noise on row level, temporal noise on column level, temporal noise on pixel level, saturation level or full-well capacity.

Not only the measurement process will be explained, but also extensive comments will be given to the obtained results. In this way the course participants learns how to do the measurements, as well as how to do the interpretation of the results generated by means of the measurements in dark.


The course is intended for engineers that already have some experience in the field.

It can be regarded as a continuation of course;

Camera lens

Fixed Pattern Noise
1. Fixed noise in dark
2. Introduction - Seeing is believing
2.1 Seeing is Believing
2.2 Seeing is Believing
2.3 Seeing is Believing 
2.4 Seeing is Believing 
2.5 Seeing is Believing 
2.6 Seeing is Believing 
Quiz - increasing the exposure time
3. Assignment
4. Theory
Quiz - Theory : Fixed-Pattern Noise
5. Results
6. Link theory measure
Quiz - true or false
7. Histograms
8. Sat level
9. Measure row fpn
10. Results Row FPN
11. Results row fpn
12. Theory column fpn
13. Results column
14. Pixel fpn
Quiz - Rows, Columns and Pixels
15. Overview conclusion

Temporal Noise
1. Assign
2. Calculation theory
3. Results temp noise
4. Theory and measurement
5. Results and graphs
Quiz - true or false
6. Row temporal noise measure
7. Row temporal noise
8. Column temporal measure
9. Results column temporal noise
10. Results pixels
11. Conclusion

Said about the course from previous participants:

"The course offers the opportunity for image sensor designers the best way to evaluate a sensor."

"Clear explanations. Interactivity."

"You could directly see results."