Super resolution for surveillance application
Surveillance activities often require zooming into a region of interest (ROI) in an image such as a face of a suspect or the number plate of a vehicle. However because of hardware limitations of image acquisition devices, the zoom would contain a lot of pixel artifacts and insufficient detail. Super...
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| Format: | Thesis |
| Published: |
2007
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| Summary: | Surveillance activities often require zooming into a region of interest (ROI) in an image such as a face of a suspect or the number plate of a vehicle. However because of hardware limitations of image acquisition devices, the zoom would contain a lot of pixel artifacts and insufficient detail. Super resolution (SR) is an image processing technique of reconstructing a high resolution (HR) image from several low resolution (LR) images. The methodology taken to achieve this can be divided into preprocessing and image processing. In preprocessing colour image frames will be selected from video footage of a scene with object movements. These images would be cropped to isolate ROI and also minimize processing time. The SR processing used is a frequency domain approach. The RGB (Red Green Blue) images will be processed as individual components and then concatenated using several function from the MATLAB Image Processing Toolbox (IPT) and also several standard MATLAB functions. The resulting SR image shows an increase of 177% in pixel count. The image also contains more detail, that can be exploited for zooming in surveillance applications. From the result analysis the optimum number of LR input images required for generating a SR image is between four to six images. This is a cut off in terms of computational efficiency and also reconstructed image quality. |
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