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Grid Image Processing Toolbox

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This Project was done in the seventh semester in a group of four as the term project for the course- Grid Computing. The goal of the project was to utilise the performance capabilities of a Computational Grid using Globus Toolkit 3 to implement some complex Digital Image Processing Algorithms. One of the algorithms implemented was the Local Histogram Equalisation.

Global Histogram Equalisation does the contrast enhancement considering the pixel values of the entire image at once. However, this may not lead to good results in non-uniformly illuminated images. It is in such cases that Local Histogram Equalisation is used. However, the complexity of this algorithm is O(n^2 m^2) where n and m are the dimensions of the image and the kernel respectively.

Input Image
Input Image
Global Enhancement
Global Enhancement (1.3s)
Local Enhancement
Local Enhancement (236 s)

The number in brackets above indicate the time taken in Image enhancement on a single desktop machine. Visibly so, Local Enhancement produces best results but has a very long execution period. It was thus implemented on a Grid to produce the desired results in less time.

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