Multi-focus Image Fusion for Visual Sensor Networks
This work addresses image fusion for visual sensor networks, but it is incremental as it builds on existing DCT methods with a specific contrast criterion.
The paper tackled the problem of multi-focus image fusion in visual sensor networks by proposing an efficient DCT-based algorithm that uses the sum of modified laplacian as a contrast criterion, resulting in improved subjective and objective quality compared to other DCT-based techniques.
Image fusion in visual sensor networks (VSNs) aims to combine information from multiple images of the same scene in order to transform a single image with more information. Image fusion methods based on discrete cosine transform (DCT) are less complex and time-saving in DCT based standards of image and video which makes them more suitable for VSN applications. In this paper, an efficient algorithm for the fusion of multi-focus images in the DCT domain is proposed. The Sum of modified laplacian (SML) of corresponding blocks of source images is used as a contrast criterion and blocks with the larger value of SML are absorbed to output images. The experimental results on several images show the improvement of the proposed algorithm in terms of both subjective and objective quality of fused image relative to other DCT based techniques.