Phase Mask Image Reconstruction for Noise-Robust Lensless Cameras
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Solution Overview
Problem
Existing camera systems face limitations in reducing size and cost due to the physical constraints of lens modules, and there is a need for improved image reconstruction techniques, especially in augmented reality devices, to overcome these constraints while maintaining image quality.
Innovation Solution
An electronic device utilizing a phase mask to modulate light, combined with an artificial intelligence model, to reconstruct images from coded images generated by the phase mask, accounting for process errors such as assembly and manufacture noise, thereby enabling consistent image reconstruction regardless of these errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a lens module is used in a camera system, then image quality can be maintained, but the size and cost of the camera module cannot be reduced due to physical limitations
Solution Approach 1:
The patent extracts and removes the lens module from the camera system, replacing it with a phase mask. This extraction eliminates the physical constraints of lens-based imaging while maintaining the core functionality of capturing light from objects, thereby reducing camera module size without sacrificing image quality
Solution Approach 2:
The patent substitutes the mechanical lens system with an optical phase mask system. Instead of using a physical lens to focus light, the invention uses a phase mask to modulate light phases, which are then reconstructed through computational algorithms. This substitution replaces a bulky mechanical component with a thin optical element and computational processing
2Manufacturing precision
If a lens module is used in a camera system, then image quality can be maintained, but the cost of the camera module increases
Solution Approach 1:
By extracting the lens module from the system, the patent eliminates a major cost driver. The phase mask is a simpler, thinner component that can be manufactured more economically than precision lens assemblies, thereby reducing overall camera module cost while maintaining image quality through computational reconstruction
Solution Approach 2:
The patent employs a phase mask that can be manufactured as a thin, inexpensive optical element compared to precision lens modules. This approach accepts the phase mask as a simpler, more cost-effective component that achieves its purpose through computational processing rather than expensive precision optics
3Volume of moving object
If a phase mask is used for lensless imaging, then camera size can be reduced, but process errors such as assembly and manufacture noise affect image reconstruction quality
Solution Approach 1:
The patent converts the harmful effect of process errors and noise into a beneficial training opportunity for the AI model. By training the neural network on data that includes various types of noise and errors, the model learns to robustly reconstruct images despite these imperfections, thereby maintaining high reconstruction quality even with a simple phase mask
Solution Approach 2:
The patent performs preliminary training of the AI model with noise and error conditions before actual image reconstruction. This preliminary action prepares the system to handle real-world imperfections in the phase mask, ensuring reliable image reconstruction despite assembly and manufacture variations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution allows for high-quality image reconstruction in a compact form factor by using a phase mask and AI model, effectively addressing the limitations of traditional camera systems and enhancing image quality in augmented reality devices.
Implementation Method 1
a mask configured to modulate a phase of incident light
Implementation Method 2
The phase mask is a very thin optical element for modulating a phase of incident light
Data Source
AI summary
Provided are an electronic device and method for reconstructing an image from a coded image. The electronic device may acquire a coded image, based on light in which a phase is modulated by a first phase mask including noise or a second phase mask not including the noise, and acquire a reconstructed image by inputting the coded image to an artificial intelligence model trained to reconstruct an image. The noise may be an error that occurred according to a process of a phase mask.