A Novel Embedded Interpolation Algorithm with Negative Squared Distance for Real-Time Endomicroscopy

Abstract

Interpolation is the most executed operation and one of the main bottlenecks in embedded imaging, registration, and rendering systems. Existing methods either lack parallelization and scalability capabilities or are too computationally complex to execute efficiently. Acknowledging that improving execution time leads to degradation in image quality, we formulate a novel Negative Squared Distance (NSD) interpolation method that exhibits excellent performance by exploiting Look-Up Table (LUT) optimization for Field Programmable Gate Array (FPGA) speedup, with a balanced trade-off in quality in our embedded endomicroscopic imaging system. Quantitative analysis on performance and resource utilization of NSD against existing methods is reported through an implementation on a Xilinx ML605 platform. Functional validation using practical image resizing and rotation applications to compare qualitative performance against existing algorithms is performed and presented with visual and numerical results. Our method is shown to have a smaller design size and produces a maximum throughput of over twofold against trilinear interpolation with on-par image quality as the baseline method.