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Computer Vision Researcher
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GPA: 4.0/4.0
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Working on solving computer vision problem using AI/ML.
Worked on low-light image denoising, image/video compression, Fast SIFT keypoint detection, image restoration like deblurring, deblocking, etc.
Engineering Computation (ECE 216/217)
Multimedia Communication(ENGR 5578), Remote Sensing (AS 280), Computer Vision (ENGR 5582)
Architected an end-to-end image compression solution utilizing deep learning techniques for complex-valued SAR images, achieving a 28% reduction in inference time.
Optimized channel context modeling through feature grouping based on latent energy, leading to a 26.5% BD-rate improvement over VVC/H.266-based SAR image compression.
Formulated a local gamma correction algorithm for adaptive face brightness adjustment in video conferencing scenarios.
Developed a machine learning-driven technique to determine the optimal tuning parameter for background exposure correction.
Accelerated real-time sports recognition and image segmentation using deep neural networks, achieving a 3× speedup for sports recognition and a 30% improvement in segmentation.
Constructed a deep neural network for image deblocking and deblurring, leveraging DCT decomposition for enhanced transform-domain processing.
Implemented deep learning-based image denoising techniques for real-world noisy images.
Proposed a dual-input-dual-output network with a dual loss function that processes noisy raw and ISP-processed noisy sRGB images to generate clean sRGB outputs.
Prototyped multi-camera image fusion techniques to improve image quality in real-time applications.
Developed an algorithm for multi-camera systems incorporating geometric calibration, image registration (SIFT), parallax correction, and alpha fusion-based blending for seamless video transitions.
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US Patent: 12224777
US Patent: 12198304
Worldwide Patent: WO2024112375A1
US Patent: 12119848
US Patent: 11967047
US Patent: 12199643
US Patent: 12229679
US Patent: 12224044
US Patent: 12068761B1
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