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High-quality reconstruction method for miniaturized lensless cameras based on imaging model optimization
Optics Express
|August 14, 2026
Summary
This study introduces a novel reconstruction method for miniaturized lensless cameras. The technique optimizes imaging models to improve scene reconstruction quality from small blur images, advancing sensor miniaturization.
Area of Science:
- Optics and Photonics
- Computer Vision
- Image Processing
Background:
- Lensless imaging's multiplexing property creates large blur images, hindering sensor miniaturization.
- Blur image truncation and reconstruction quality present a conflict, limiting device size reduction.
Purpose of the Study:
- To develop a high-quality reconstruction method for miniaturized lensless cameras.
- To overcome limitations in sensor miniaturization caused by blur image size and reconstruction challenges.
Main Methods:
- Image model optimization incorporating a cropping operator to overdetermine reconstruction equations.
- Utilizing an improved Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) for initial reconstruction.
- Employing a U-Net network to enhance the perceptual quality of reconstructed images.
Main Results:
- The proposed method achieves more stable and higher-quality scene reconstruction with small blur images.
- Outperforms traditional optimization and deep learning reconstruction algorithms on simulated and real-world datasets (OCIFAR100_sim, OCIFAR100, PhlatCam).
Conclusions:
- The developed method provides a viable pathway for miniaturizing lensless camera sensors.
- Offers theoretical support and technical solutions with significant practical application value for compact imaging systems.
