Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64678
Title: Design and implementation of a single-frame super-resolution processing alogrithm for lensless microfluidic imaging system
Authors: Xu, Hang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2015
Abstract: The contact-image based microfluidic cytometer with machine-learning for single-frame super-resolution processing is used to count and recognize multiple types of cells in clinical diagnostics and biological research As an extension research, this paper proposed a new algorithm that combines external example-based sparse representation and convolutional neural network. Compared with extreme learning machine algorithm in terms of PSNR, SSIM and running time, the proposed algorithm can perform better in recovering high-resolution images at the expense of more running time. In addition, with the aim to running this system in FPGA, the ELM-SR should be programmed in Verilog. The first step is to transferring the MATLAB codes to C++ code, which has been done in this paper. The result is satisfactory and the difference is analysed in terms of MATLAB and C++ property. As an extension research, this paper not only explored more deeply based on previous job, also made a good preparation for future work.
URI: http://hdl.handle.net/10356/64678
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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