In this paper, we propose an occlusion removal method using sub-image block matching for improved recognition of partially occluded 3D objects in computational integral imaging (CII). When 3D plane images are reconstructed in CII, occlusion degrades the resolution of reconstructed images. To overcome this problem, we apply the sub-image transform to elemental image array (EIA) and these sub-images are employed using block matching method for depth estimation. Based on the estimated depth information, we remove the unknown occlusion. After completing the occlusion removal for all sub-images, we obtain the modified EIA without occlusion information through the inverse sub-image transform. Finally, the 3D plane images are reconstructed by using a computational integral imaging reconstruction method with the modified EIA. The proposed method can provide a substantial gain in terms of the visual quality of 3D reconstructed images. To show the usefulness of the proposed method we carry out some experiments and the results are presented.

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Computational integral imaging method can digitally provide a series of plane images of three-dimensional (3D) objects. However, the resolution of 3D reconstructed images is dramatically degraded as the distance from the lenslet array increases. In this paper, to overcome this problem, we propose a novel computational integral imaging reconstruction (CIIR) method based on smart pixel mapping (SPM). Since SPM is a computational process in which elemental images recorded at long distances are convertible to ones recorded near lenslet array, this can give us the improved resolution of plane images for 3D objects located at a long distance range from a lenslet array. For the effective use of the SPM-based CIIR method, we design a novel two-stage CIIR method by the combined use of the conventional CIIR and the SPM-based one. The conventional CIIR method is applied over a short distance range, while the SPM-based CIIR is used over a long distance range. We carry out some experiments to verify the performance of the two-stage CIIR system. From the experimental results, the proposed system can provide a substantial gain over a long distance range in terms of the resolution of reconstructed plane images.

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This paper presents an image quality enhancement of computational integral imaging reconstruction (CIIR) method by using a binary weighting mask on occlusion areas in elemental images. The proposed method utilizes a block-matching algorithm to estimate the occlusion areas in elemental images. Then, a binary weighting mask generated from the estimated occlusion area is applied to our CIIR method. This minimizes the overlapping effect of occluding objects in the reconstructed plane images and thus improves visual quality dramatically. To show the usefulness of our proposed scheme, we conduct several experiments and present the results. The experimental results indicate that our method is superior to the existing methods.

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We describe a computational method for depth extraction of three-dimensional (3D) objects using block matching for slice images in synthetic aperture integral imaging (SAII). SAII is capable of providing high-resolution 3D slice images for 3D objects because the picked-up elemental images are high-resolution ones. In the proposed method, the high-resolution elemental images are recorded by moving a camera; a computational reconstruction algorithm based on ray backprojection generates a set of 3D slice images from the recorded elemental images. To extract depth information of the 3D objects, we propose a new block-matching algorithm between a reference elemental image and a set of 3D slice images. The property of the slices images is that the focused areas are the right location for an object, whereas the blurred areas are considered to be empty space; thus, this can extract robust and accurate depth information of the 3D objects. To demonstrate our method, we carry out the preliminary experiments of 3D objects; the results indicate that our method is superior to a conventional method in terms of depth-map quality.