The normalized random map of gradient for generating multifocus image fusion

• Main Authors: Ismail, ., Kamarul Hawari, Ghazali
• Format: Article
• Language: English
• Published: Blue Eyes Intelligence Engineering & Sciences Publication 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/37774/1/The%20Normalized%20Random%20Map%20of%20Gradient%20for%20Generating%20Multifocus%20Image%20Fusion_IJRTE.pdf; http://umpir.ump.edu.my/id/eprint/37774/; https://doi.org/10.35940/ijrte.F9904.059120; https://doi.org/10.35940/ijrte.F9904.059120
• Institution: Universiti Malaysia Pahang

The multifocus image fusion is a kind of method in image processing to collecting the sharp information from multifocus image sequence. This method is purposed to simplify the reader understand the complex information of image sequence in an image only. There are many methods to generate fused image from several images so far. Many researchers have developed many new and sophisticated methods. They show complicated computation and algorithm. So, that it is difficult to understand by the new students or viewer. Furthermore, they get difficulties to create the new one. In order to handle this problem, the proposed method a concise algorithm which is able to generate an accurate fused image without using a complicated mathematical equation and tough algorithm. The proposed method is the normalized random map of gradient for generating multifocus image fusion. By generate random map of gradient, the algorithm is able to specify the coarse focus region accurately. The random map of gradient is a kind of information formed independently from independent matrix. This data has a significant role in predict the initial focus regions. The proposed algorithm successes to supersede difficulties of mathematical equations and algorithms. It successes to eliminate the mathematical and algorithm problems. Furthermore, the evaluation of proposed method based on the fused image quality. The Mutual Information and Structure Similarity Indexes become our key parameter assessment. The results show that the outputs have high indexes. It means it is acceptable. Then the implementation of multifocus image fusion will increase the quality of the applied fields such as remote sensing, robotics, medical diagnostics and so on. It is also possible implemented in other new fields.