Camera electronics and image enhancement software for infrared detector arrays

Date

2012

Author

Küçükkömürler, Alper

Metadata

Show full item record

Item Usage Stats

17
views

6
downloads

Cite This

This thesis aims to design and develop camera electronics and image enhancement software for infrared detector arrays. It first discusses the camera electronics suitable for infrared detector arrays, then it concentrates on image enhancement software that are implemented including defective pixel correction, contrast enhancement, noise reduction and pseudo coloring. After that, testing and results of the implemented algorithms were presented. Camera electronics and circuit operation frequency are selected considering the available standard programmable devices and the output rate of the detector readout circuitry. The target device for implementation of algorithms was Xilinx Spartan – 3 XC3S1500 which is used in the camera tests at METU-MEMS Research and Applications Center. Considering the real time operation, the target clocking frequency for operation of the circuitry was selected as 2MHz. Image enhancement algorithms primarily aim to be implemented for 320 x 240 resolution detectors, however with parametric implementation, they aim to support other resolutions, including 160 x 120 and 640 x 512. In addition, all implementations aim to be modular and reusable. Various different approaches are used for image enhancement software: (i) defective pixel correction is achieved by using a selective median filtering approach, (ii) contrast enhancement is achieved by employing contrast stretching and histogram based methods, and (iii) noise reduction is achieved by implementing a spatial filter. In addition to these, four types of pseudo coloring methods were applied and tested. Test results show that defective pixel correction algorithm operates at 20.0 MHz, with 0.0 x 10-3 RMS error from its MATLAB prototype, and contrast enhancement algorithms are able to operate at 3.3 MHz, with an average of 545.0 x 10-3 RMS error. Spatial filtering for noise reduction operates at 20.0 MHz, with a 2.6 x 10-3 RMS. Pseudo-coloring operates at 125.0 MHz, with a 0.0 x 10-3 RMS deviation from its MATLAB prototype,

Subject Keywords

Image processing

URI

http://etd.lib.metu.edu.tr/upload/12614106/index.pdf
https://hdl.handle.net/11511/21412

Collections

Graduate School of Natural and Applied Sciences, Thesis