Abstract
Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion. Particle Swarm Optimization (PSO) algorithm has been used to find optimal size of the block of the images for extraction of focus measure features. After finding optimal blocks, three focus measures Sum of Modified Laplacian, Gray Level Variance and Contrast Visibility has been extracted and combined these focus measures by using intelligent fuzzy technique. Fuzzy based hybrid intelligent focus values were estimated using contrast visibility measure to generate focused image. Different sets of multi-focus images have been used in detailed experimentation and compared the results with state-of-the-art existing techniques such as Genetic Algorithm (GA), Principal Component Analysis (PCA), Laplacian Pyramid discrete wavelet transform (DWT), and aDWT for image fusion. It has been found that proposed method performs well as compare to existing methods.
Highlights
There is a significant role of image fusion in current state of the art technology such asRobot Vision, Object Recognition, Target Detection, Satellite Imaging, surveillance and medical
In order to see the efficiency of our fusion technique with existing techniques, we used two different sets of quantitative measures
4.1.1 Mutual Information (MI) Mutual information is an important measure which is often used in multi-focus image fusion
Summary
There is a significant role of image fusion in current state of the art technology such as. These can be due to external and internal restrictions of the image acquisition instruments Some of their limitations are low lighting conditions, limited focusing power of the cameras and motion of some objects within the scene. The sensors are planer image detector like CCD arrays i so if the objects are curved in shape; the image will be partially focused and partially defocused This suggests that certain parts of the picture are in focus while the others are out of focus. Based on Gaussian model, defocused image represented by g(x, y) can be modeled by convolving Point Spread Function (PSF) of the camera with the focused image (x, y). (a) The sensor and image plane are not aligned (b) The lens is not static (c) The object and object plane do not remain aligned
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