Abstract
All-in-focused image combination is a fusion technique used to acquire related data from a set of focused images at different depth levels, which suggests that one can determine objects in the foreground and background regions. When attempting to reconstruct an all-in-focused image, we need to identify in-focused regions from multiple input images captured with different focal lengths. This paper presents a new method to find and fuse the in-focused regions of the different focal stack images. After we apply the two-dimensional discrete cosine transform (DCT) to transform the focal stack images into the frequency domain, we utilize the sum of the updated modified Laplacian (SUML), enhancement of the SUML, and harmonic mean (HM) for calculating in-focused regions of the stack images. After fusing all the in-focused information, we transform the result back by using the inverse DCT. Hence, the out-focused parts are removed. Finally, we combine all the in-focused image regions and reconstruct the all-in-focused image.
Highlights
Light field cameras, called plenoptic cameras, have been popularly used in digital refocusing and three-dimensional reconstruction
The experimental parameters are assigned as a discrete cosine transform (DCT) window size of 8, a sum of the updated modified Laplacian (SUML) window size of 3, a harmonic mean (HM) window size of 3, a CV window size of 3, and TSUML of 10
We proposed an all-in-focused image combination method by integrating the SUML, Enhanced SUML (eSUML), and HM in the DCT domain
Summary
Called plenoptic cameras, have been popularly used in digital refocusing and three-dimensional reconstruction. Since the light field image generates a set of images focused at different depth levels after being captured, it is suggested that one can determine objects in the foreground and background regions It can generate a set of multi-view images without the need for calibration images. Sci. 2019, 9, 3752 suggested a multi-focus image fusion method for visual sensor networks in the discrete cosine transform (DCT) domain [4]. Since it Since is difficult to classify in-focused and advantages of image representation in the frequency it is difficult to classify in-focused out-focused regions in the in spatial domaindomain when edges the out-focused parts are sharp, we sharp, transform and out-focused regions the spatial whenofedges of the out-focused parts are we images intoimages the frequency domain to analyze the image information. Reduction process the spatial domain that requires a complexity execution
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