Fusion of Multi-Focus Images using Jellyfish Search Optimizer


Çıtıl F., Kurban R., Durmuş A., Karaköse E.

5 th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2022, Ankara, Turkey, 28 - 29 May 2022, pp.61

  • Publication Type: Conference Paper / Full Text
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.61

Abstract

When obtaining on image a scene, the lens focuses of objects at a certain distance, and objects at other distances are blurred. This is called the limited depth of field problem. A approach for solving this problem is multi-focus fusion picture. A clear view of the entire scene is obtained by using the multi-focus image fusion method. For this method, at least two images captured at different focuses are combined using image fusion methods. Various algorithms have been developed for multi-focus image fuion methods. For multifocus image fusion, pixel-level block-based methods are commonly used. The block size is a factor that significantly affects the fusion performance. As a result, the block size parameter must be improved. The Jellyfish optimization algorithm (JSA) is used to propose a block-based multifocus picture fusion approach based on the optimal selection of clearer image blocks from source images. The results of DWTPCA, DCHWT, APCA, PCA, SWTDWT and SWT methods, which are traditional image fusion methods, and ABC (artificial bee colony) and JSA optimization algorithms, which are metaheuristic methods, are compared. In addition, it has been determined that the JSA method has better performance than other traditional methods when compared both visually and quantitatively