No abstract

Image fusion is a process of combining data from multiple sources to achieve refined or improved information for making decisions. It has many applications. When we use images with a similar acquisition time, the expected result is to obtain a fused image that retains the spatial resolution from the panchromatic image and color content from the multi-spectral image. In recent time different methods have been developed. These methods are both in spatial domain and in wavelet domain. Out of these two the wavelet domain based methods are more suitable as they are capable to handle the spatial distortion produced by the spatial domain. In this paper the proposed method is compared with principle component analysis, discrete cosine transform and also with biorthogonal wavelet transform in which bilateral filter and adaptive histogram is not present. This comparison is on the bases of different parameters. Biorthogonal wavelet transform is capable to preserve edge information and hence reducing the distortions in the fused image. It has two important properties wavelet symmetry and linear phase which are not present in spatial domain. The performance of the proposed method has been extensively tested on several pairs of multi-focus and multimodal images. Experimental results show that the proposed method improves fusion quality by reducing loss of significant information available in individual images.

Image fusion is a technique which is used to fuse two or more images into a single image. It is one of the most significant technique in digital image processing. There are number of techniques present today which are used to fuse images. The objective of these techniques is to fuse the images together to produce a more elucidative image. Out of all the techniques used the more valuable techniques are DCT and BWT. These two techniques are time saving in real-time classification. The main objective are this paper is to find the best digital image fusion algorithm among DCT, PCA and BWT.