<![CDATA[Images are merged to produce a single image with increased image quality and the integrity of key characteristics while combining complementing multi-temporal, multi-view, and multi-sensor information. The goal of the study is to enhance the focus of three wavelet transform methods, namely image fusion based on discrete wavelet transform, stationary wavelet transform, and dual tree-complex wavelet transform, in order to improve the quality of medical images such as computed tomography images, magnetic resonance images, and the quality of merging visible images and infrared images using the technique of image fusion based on wavelet transform. The fuzzy histogram equalization method, the Lucy-Richardson algorithm, the recovery of the pictures prior to the fusion process, and the convolutional filters based on linear spatial filters were all utilized for the optimization process. Seven scales were employed in the study to evaluate the performance effectiveness of the suggested strategies and to contrast them with the conventional approaches. The results showed that, when compared to the other methods, image fusion based dual tree-complex wavelet transform and spatial filters produced the best results. This paper discusses numerous state-of-the-art image fusion techniques at various levels, along with their benefits and drawbacks, as well as various spatial and transform-based techniques with quality measures and their applications in many fields. This review has finished with a number of future directions for various image fusion applications.]]>
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