Resolution, Noise, Dynamic Range | Image Sensing

First Principles of Computer Vision is a lecture series presented by Shree Nayar who is faculty in the Computer Science Department, School of Engineering and Applied Sciences, Columbia University. Computer Vision is the enterprise of building machines that “see.” This series focuses on the physical and mathematical underpinnings of vision and has been designed for students, practitioners, and enthusiasts who have no prior knowledge of computer vision.

[@konradwelz8752]

This is a totally stunning series on digital imaging. Beautifully paced, clearly articulated, and excellently illustrated. Thank you for making these.

[@yubakrarai]

Very well explained ! thank you

[@alessiostefanelli5663]

Great material !

[@christianbauermeister6266]

thanks! very interesting and well explained👍

[@nasifmdtanjim]

Absolutely amazing content. Thanks 🙏

[@atulmathur8486]

Good work Sir

[@SuperGenitalz]

Solid!

[@sakirkandemir9657]

''' THANKS ...SUPERRRRRRRR.....!

[@kendiato8714]

thank you very much!

[@drantsplants]

Variance of noise is Delta^2/12 @8:40

[@user-bv5eu3zk5w]

Regarding the dynamic range definition
In electronics,when comparing voltage we use 20log(*) & when comparing power we use 10log(*)
Why in image processing the dynamic range of energy comparison is using 20log(*) & not 10log(*)?

[@Hanniballectraa]

I had a question:Given that Bmax is the filling up of a potential well, wouldn't that be based on the material being used as the sensor?
Then, how can the amount of exposure time possible for a frame in a video determine a low dynamic range of the sensor?

[@ahmedreda8333]

Around min 8:42, I think the variance of the quantization noise is delta squared over 12 (not delta/12).

[@brunolec]

no, this is correct

[@vgikovecable]

How does the size of the matrix affect the dynamic range?

[@renyvincent4406]

Var should be (Delta*Delta) / 12

[@derekthompson2301]

do we have to understand digital processing or something in order to understand this ? it's confusing and I feel like he skipped some important concepts

[@km-sc4kz]

in the parts about shot noise, and how photons arrive at random intervals- the poisson distribution comes up. i don't know what proof we have that it follows such a distribution. but assuming that is true, to understand the noise in the signal due to such a process- you should look into the mathematics of arrival processes, bernoulli random processes and poisson random processes.

[@WiteBot]

Dry textbook theory. Needs to be connected with actual image sensor examples

[@jaramillosotobernardojesus4438]

Min. 13:00, When he says that digital cameras have 72.2 dB today, Is he talking in 2021?