Research and design efficient digital image processing pipelines for CMOS image sensors by using C++, Python, and Matlab.
Optimize the PD Shadow Correction block to ensure compatibility with various sensor patterns, such as the 4C pattern and Bayer Pattern.
Perform analysis and optimization of the noise reduction modules, aiming to achieve electrical power savings and cost reduction for image sensors.
Implement 3D denoising algorithms, such as the Multi-frame denoising algorithm, to gain higher performance on RAW image series.
Improve NLM denoising algorithm to gain higher quality on color images, while maintaining electrical efficiency.
Perform analysis, modeling, and simulation of the imaging pipeline.
Evaluate and tune existing image processing pipeline and verify performance on FPGA and hardware.
Cooperate with the quality assurance team to customize algorithms for different implements.
Collect large image datasets under diverse environment settings.
Write algorithms documentation, emphasizing their electrical implication.
Requirements & Skills:
Master’s degree in Electrical Engineering, Computer Engineering, or related fields with courses work in Digital Signal Processing, Image Processing, Computer Vision, Data Structures, Pattern Recognition, and Machine Learning.
Will accept foreign equivalent Master’s degree.
Must possess skills in color imaging, image quality, image processing, image quality measurement, camera systems and bench testing, image algorithms, ISP algorithms, Matlab, Python, and C++.
