Image Fusion Approach with NOISE REDUCTION USING GENETIC ALGORITHM & Sure-let Algorithm
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 3, Issue 6
Abstract
Fusing information contained in multiple images plays an increasingly important role for quality inspection in industrial processes as well as in situation assessment for autonomous systems and assistance systems. The aim of image fusion in general is to use images as redundant or complementary sources to extract information from them with higher accuracy or reliability. In our research paper we introduce a new approach that begin with image denoising using GA and then apply the Sure-let transform for image decomposition to get a multi focus image fusion image that is focused in all of its parts. The results show that Curvelet transform had been proven to be effective at detecting image activity along curves, and increasing the quality of the obtained fused images. GA shows more accurate results in image de-noising. We performed image fusion using Curvelet Transform with Genetic Algorithm and Sure-let algorithm for image denoising. This new method has reached an optimum fusion result. For the implementation of this proposed work we use the Image Processing Toolbox under Matlab Software
Authors and Affiliations
Jaspreet Kaur *
Keywords
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