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Summary

This summary is machine-generated.

We introduce a novel method for joint sensor and neural network design. This approach optimizes optical parameters and neural networks together for improved image restoration and end-to-end system design.

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Area of Science:

Optics and Photonics
Computer Vision
Machine Learning

Background:

Traditional optical system design is often separate from image processing.
Optimizing sensor parameters and neural networks concurrently can enhance image quality and system performance.

Purpose of the Study:

To propose a new method for jointly designing optical sensors and their neural network-based processing.
To enable end-to-end optimization of lens parameters and neural network weights.

Main Methods:

Utilized a differential ray tracing (DRT) model to simulate the sensor's point-spread function (PSF) and its derivatives.
Employed gradient backpropagation for joint optimization of lens parameters and neural network weights.
Validated the method for image restoration tasks, specifically focus setting optimization.

Main Results:

Demonstrated the feasibility of joint optical and processing optimization.
Provided interpretations for the optimization results in focus setting scenarios.
Showcased the method's ability to optimize lens parameters across the full field of view.

Conclusions:

The proposed method facilitates end-to-end design of neural networks and lenses.
This approach allows for the integration of all optical parameters within the full sensor field of view.
Opens new avenues for designing integrated optical and computational imaging systems.