Multi-Focus Image Fusion in DCT Domain using Variance and Energy of Laplacian and Correlation Coefficient for Visual Sensor Networks

The purpose of multi-focus image fusion is to gather the essential information and the focused parts from the input multi-focus images into a single image. These multi-focus images are captured with different depths of focus of cameras. A lot of multi-focus image fusion techniques have been introduced using the focus measurement in the spatial domain. However, multi-focus image fusion processing is very time-saving and appropriate in discrete cosine transform (DCT) domain, especially when JPEG images are used in visual sensor networks. Thus most of the researchers are interested in focus measurement calculations and fusion processes directly in the DCT domain. Accordingly, many researchers have developed some techniques that substitute the spatial domain fusion process with the DCT domain fusion process. Previous works on the DCT domain have some shortcomings in the selection of suitable divided blocks according to their criterion for focus measurement. In this paper, calculation of two powerful focus measurements, energy of Laplacian and variance of Laplacian, are proposed directly in the DCT domain. Moreover, two other new focus measurements that work by measuring the correlation coefficient between the source blocks, and the artificial blurred blocks are developed completely in the DCT domain. However, a new consistency verification method is introduced as a post-processing, significantly improving the quality of the fused image. These proposed methods significantly reduce the drawbacks due to unsuitable block selection. The output image quality of our proposed methods is demonstrated by comparing the results of the proposed algorithms with the previous ones.