In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors

Robotic vision has traditionally relied on high-performance yet resource-intensive computing solutions, which necessitate high-throughput data transmission from vision sensors to remote computing servers, sacrificing energy-efficiency and processing speed. A promising solution is data compaction through contour extraction, visualizing only the outlines of objects while eliminating superfluous backgrounds. Here, we introduce an in-sensor multi-level image adjustment method using adjustable synaptic phototransistors, enabling the capture of well-defined images with optimal brightness and contrast suitable for achieving high-clarity contour extraction. This is enabled by emulating dopamine-mediated neuronal excitability regulation mechanisms. Electrostatic gating effect either facilitates or inhibits time-dependent photocurrent accumulation, adjusting photo-responses to varying lighting conditions. Through excitatory and inhibitory modes, t..., , , # In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors

[https://doi.org/10.5061/dryad.bcc2fqzpt](https://doi.org/10.5061/dryad.bcc2fqzpt)

## Description:

This dataset supports the study titled \"In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors.\" It includes Python scripts and associated data for preprocessing and analyzing image data using the parameter of synaptic phototransistor and in-sensor multilevel image adjustment method.

## **Directory and File Structure**

* Adaptive_thresholding.py
* Merge_using_logic_operation.py
* Nonlinear_optoelectronic_conversion.py
* Sobel_edge_detection.py
* synaptic_phototransistor_parameter_for_nonlinear_optoelectronic_conversion.xlsx
* \[raw_csv_data_folder] Folder with raw CSV image data (not provided here)
* \[output_folders] Output folders for processed images (not provided here)

### **Processing Pipeline and Sc...,