Multi-focus image fusion by local optimization over sliding windows

Garnica-Carrillo, Adan; Calderón, Félix; Flores, Juan J.

doi:10.1007/s11760-017-1229-x

Cited by 7 publications

(1 citation statement)

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“…In order to sharpen the focus region and decrease computation cost, the applying Laplacian kernel is implemented in [5]. Then, the optimization method of kernel processing and subsampling sample is applied in [6]. The other method predicts the focus region through the mean shift algorithm to create the precise segmentation map [7].…”

Section: Introductionmentioning

confidence: 99%

Multi Focus Image Fusion with Region-Center Based Kernel

Ismail

Hawari

2021

Int. J. Adv. Sci. Eng. Inf. Technol.

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The usage of the camera is ubiquitous nowadays. It provides highly accurate information. Then, the camera helps humans to carry out their specific tasks correctly. Furthermore, the camera becomes an important tool to achieve accurate computation in some fields, such as in medical diagnostic, robotics, remote sensing, and others. On the other side, the camera also has a weakness to capture detailed information of the scene in one image. Many images are needed to obtain the focus information of all the scenes since the lens's limitation depth of field produces out of focus regions beyond the focused object. To make a settlement of that case, the researchers have found a multi-focus image fusion process. This process selects all detailed information from a sequence of images and fuses them into one focused image. Through this fused image, the user such as human and machine can read the focus information easier. Later, the researchers developed multi-focus image fusion methods which various advanced procedures and algorithms. Furthermore, the implementation of multi-focus image fusion in new fields multiplied in the last two decades ago. It was able to create an accurate and efficient method to build a fused image. The proposed method is a kind of a new method in multi-focus image fusion. It works according to the region-center based kernel. The kernel processes input image to predict the detailed information of the scene. This method is robust to prevent the unexpected effects and sensitivities of noise. The proposed method generates a fused image with high accuracy. Finally, the assessment is done based on mutual information and structure similarity.

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Section: Introductionmentioning

confidence: 99%