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In-Sensor Compressed Imaging with Reconstruction-Free Recognition via Ferroelectric Photodiodes.

Tao Yan^1,2, Yuchen Cai^3,4, Can Wang^1,2

¹Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces an efficient machine vision system using ferroelectric sensors for in-sensor image compression and neural network processing. This approach merges sensing and computation, enhancing machine vision capabilities with reduced data redundancy.

Keywords:

Haar wavelet transform ferroelectric photodiodes in-sensor compressed imaging machine vision

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Area of Science:

Materials Science
Computer Vision
Electrical Engineering

Background:

The shift towards machine-centric vision necessitates advanced imaging systems.
Compressed imaging offers a solution for data redundancy by capturing essential visual information during sampling.
Integrating compressed imaging at the sensor level is crucial for efficient data processing.

Purpose of the Study:

To develop an efficient machine vision strategy by combining in-sensor compressed imaging with neural networks.
To leverage ferroelectric BiFeO3 photodiodes for nonvolatile photoresponse states and image compression.
To enable reconstruction-free deep processing for enhanced machine vision.

Main Methods:

Utilized BiFeO3 photodiodes with tunable ferroelectric polarization to achieve multiple nonvolatile photoresponse states.
Applied the Hadamard product for transforming spatial image data into the Haar wavelet domain for in-sensor compression.
Fed compressed data directly into neural networks, bypassing traditional image reconstruction steps.

Main Results:

BiFeO3 photodiodes demonstrated 113-274 stable, linear nonvolatile photoresponse states with 85% yield.
Achieved high simulated neural network accuracy (87.0% at 0.04 compression ratio, 95.8% at 0.9).
Successfully demonstrated in-sensor image compression and reconstruction-free deep processing.

Conclusions:

The proposed strategy effectively merges image sensing and compression within ferroelectric sensors.
This integrated approach offers an efficient solution for machine vision by enabling direct deep processing of compressed data.
The technology paves the way for more streamlined and powerful machine vision systems.