A Review on an approach for Multifocus Digital Image Fusion using Multiscale Image Decomposition

Image fusion aims at improving spectral information in a fused image as well as adding spatial details to it. Among the existing fusion algorithms, filter-based fusion methods are the most frequently discussed cases in recent publications due to their ability to improve spatial and spectral information of images. Filter-based approaches extract spatial information from the original image and inject it into another images. Designing an optimal filter that is able to extract relevant and non-redundant information from the original image is presented in this letter. The optimal filter coefficients extracted from statistical properties of the images are more consistent with type and texture of the remotely sensed images compared with other kernels such as wavelets. Visual and statistical assessments show that the proposed algorithm clearly improves the fusion quality in terms of correlation coefficient, relative dimensionless global error in synthesis, spectral angle mapper, universal image quality index, and quality without reference. The visual assessment will show that the proposed method provides more spectral and spatial information. In addition to the visual inspection, the efficiency of each method will quantitatively analyze in both the original and inferior levels. This paper presents a method of image fusion based on wavelet transform. The designed method improves the definition and the special quality of image and is more suitable for multiscale properties of human vision system. Image fusion aims at improving spectral information in a fused image as well as adding spatial details to it. Among the existing fusion algorithms, filter-based fusion methods are the most frequently discussed cases in recent publications due to their ability to improve spatial and spectral information of images. Filter-based approaches extract spatial information from the original image and inject it into another images. Designing an optimal filter that is able to extract relevant and non-redundant information from the original image is presented in this letter. The optimal filter coefficients extracted from statistical properties of the images are more consistent with type and texture of the remotely sensed images compared with other kernels such as wavelets. Visual and statistical assessments show that the proposed algorithm clearly improves the fusion quality in terms of correlation coefficient, relative dimensionless global error in synthesis, spectral angle mapper, universal image quality index, and quality without reference. The visual assessment will show that the proposed method provides more spectral and spatial information. In addition to the visual inspection, the efficiency of each method will quantitatively analyze in both the original and inferior levels. This paper presents a method of image fusion based on wavelet transform. The designed method improves the definition and the special quality of image and is more suitable for multiscale properties of human vision system.