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Mostly, shape from focus (SFF) methods utilize initial depth estimate to obtain 3D shape of an object. However, accuracy of these methods is limited due to erroneous initial focus and depth measurements. In this paper, we introduce a Gaussian process regression based approach, which estimates 3D shape of the object from the noisy initial depth values and focus measurements. Initial depth is estimated...
Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently,...
Mostly researchers use all pixels within a window to filter out the impulse noise. They increase the size of neighboring pixels with the increase of noise density. However, this estimate of all neighboring pixels does not give promising results for high level of noise density. In contrast, in the paper, we propose impulse noise removal scheme that emphasizes on few noise-free pixels. The proposed...
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