Biorthogonal Wavelet Transform Using Bilateral Filter and Adaptive Histogram Equalization

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.

Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

“…Many image fusion techniques have been developed such as, maximu m, minimu m, average methods, principal component analysis (PCA) [26,30,31], intensity-huesaturation [28]. In above discussed methods , average method provides the average information of the images but weighted average methods provide the informat ion according to their weights and produce better fusion results.…”