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Does deep learning always outperform simple linear regression in optical imaging?

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Area of Science:

Optical imaging
Machine learning
Image reconstruction

Background:

Deep learning (DL) is widely used in optical imaging.
Limitations of DL in optical imaging are not well-studied.
Optical systems are often linear.

Purpose of the Study:

Investigate DL limitations in optical imaging.
Compare DL with linear regression methods.
Identify optimal methods for optical imaging problems.

Main Methods:

Comparative analysis of deep learning and linear regression.
Evaluation on two black-box optical imaging problems.
Assessment of method performance with varying training sample sizes.

Main Results:

Linear regression outperformed DL in specific optical imaging tasks.
DL showed weaknesses with small training datasets.
Analysis revealed DL's nonlinearity may be unsuitable for linear systems.

Conclusions:

Linear regression can be superior to DL in certain optical imaging scenarios.
DL's effectiveness is data-dependent, particularly with limited samples.
Method selection should consider the inherent linearity of optical systems.