About Ng Suit Mun
Hello! I'm Mun, a passionate researcher in image processing specializing in Super Resolution techniques. My work focuses on a groundbreaking hybrid approach that combines the strengths of dictionary learning and deep learning SR methods. Through the application of Super Resolution, I contribute to advancing smart agriculture by improving the accuracy of pest monitoring systems, ultimately supporting more sustainable farming practices.
Living and working across Malaysia and Japan has broadened my cultural horizons and enriched my professional experience with diverse research methodologies and technological innovations. Fluent in English, Malay, Chinese, and Japanese, I've seamlessly engaged with international teams, contributing to the global advancement of image processing technologies. I am excited about the future of imaging technology and look forward to contributing to its evolution through innovative research and cross-cultural collaboration.
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Previous Project
Simple Illustration of Super-Resolution (SR) Technique
Current Project
Demo of Thrips Detection Application
HYBRID IMAGE RECONSTRUCTION ALGORITHM FOR LOW-RESOLUTION IMAGES
A novel hybrid technique was proposed that combines the quick contrast enhancement of dictionary learning SR methods for grayscale images with the color image processing strengths of deep learning SR techniques.
DETECTION AND CLASSIFICATION OF THRIPS
We apply the proposed dictionary-based SR method, KSVD_DR, to real-world agricultural challenges. Its advantages include not needing large image datasets and having shorter training periods due to its simpler structural design. It specifically addresses the need for efficient detection and classification of thrips in farming. This application proves the practical utility of the research, offering a viable solution to the critical issue of pest management in agriculture.