Camera System

Period: 2016 – 2018
Scope: Camera Electro-Optical Systems
Tools: MATLAB

Overview

Worked on low-level camera imaging pipeline and electro-optical system components, focusing on image quality optimization, sensor calibration, and real-time processing algorithms.

This role built the foundation for later work in computer vision and deep learning by developing a strong understanding of:

• sensor physics
• image signal processing (ISP)
• noise modeling and correction
• performance optimization under hardware constraints

Imaging Pipeline Overview

flowchart LR
    A[Sensor Raw Data] --> B[NUC]
    B --> C[BPR]
    C --> D[DNR]
    D --> E[DDE]
    E --> F[Final Image Output]

    subgraph Calibration
        G[Dark Frame]
        H[Flat Field]
    end

    G --> B
    H --> B

Key Contributions

• Built sensor calibration pipelines to improve image consistency and reliability
• Collaborated with hardware teams on sensor-level improvements
• Developed tools for quantitative evaluation of image quality
• Optimized algorithms for real-time or near real-time performance
• Designed and implemented core image enhancement algorithms for camera systems

Works

• Camera & Sensor Test Bench
• Non-Uniformity Correction (NUC)
• Bad Pixel Replacement (BPR)
• Digital Noise Reduction (DNR)
• Digital Detail Enhancement (DDE)

Tech Focus

Sensor Calibration · Camera ISP · Signal Processing · Image Processing · MATLAB

Impact

• Improved overall image quality consistency across sensors
• Reduced noise and artifacts in low-light conditions
• Established reusable algorithmic components for camera pipelines
• Strengthened cross-domain expertise bridging hardware and software