Abstract
Image Homogenization is an essential part in image processing environment, which allows stitching of multiple images with the same background. Homogenization is a basic idea which combines various multiple images having similar characteristics. The software of the system will help to homogenize multiple images by taking them as input images that depicts similar matching features specifically the background and it will combine the images to form a single panoramic image. However, Image processing is a domain that will help in homogenizing multiple images automatically with the help of various homogenizing algorithms. Mainly, homogenization concept is used for the “Image Stabilization” that is featured in camcorders which uses frame-rate image alignment and for the high-resolution images. We aimed for creating an architecture for homogenizing multiple images which results in a panoramic image as an output which is executed using python. Various images with similar key points are collected from various image data sources. The data collected from different sources is aggregated, processed and compared and further analyzation will take place through image processing. The proposed architecture will be able to handle multiple images, efficiently. Also, from the existing previous image homogenizing process, it is capable of stitching a greater number of images with matching the features, correlation and basic details of the image obtained from different sources, identifying patterns which may help in homogenizing of the image automatically. Besides an architecture description, we present feasibility results based on the experiments performed on rendering and overlapping of image. Image homogenizing are essential feature in image processing environment, allowing for stitching of two or more than two most similar images. By image homogenizing it is even possible to homogenize medical images as well as satellite images due to these high-resolution images can be captured automatically through this technique.
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