Computational integral imaging reconstruction method of 3D images using pixel-to-pixel mapping and image interpolation
ABSTRACT In this paper, we propose a novel computational integral imaging reconstruction (CIIR) method to improve the visual quality of the reconstructed images using a pixel-to-pixel mapping and an interpolation technique. Since an elemental image is magnified inversely through the corresponding pinhole and mapped on the reconstruction output plane based on pinhole-array model in the conventional CIIR method, the visual quality of reconstructed output image (ROI) degrades due to the interference problem between adjacent pixels during the superposition of the magnified elemental images. To avoid this problem, the proposed CIIR method generates dot-pattern ROIs using a pixel-to-pixel mapping and substitutes interpolated values for the empty pixels within the dot-pattern ROIs using an interpolation technique. The interpolated ROIs provides a much improved visual quality compared with the conventional method because of the exact regeneration of pixel positions sampled in the pickup process without interference between pixels. Moreover, it can enable us to reduce a computational cost by eliminating the magnification process used in the conventional CIIR. To confirm the feasibility of the proposed system, some experiments are carried out and the results are presented.
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ABSTRACT: In this paper, an image interpolation method based on even-odd decomposition (EOD) is proposed. An input signal for interpolation is decomposed to even and odd vectors by EOD. And then different interpolation methods are applied to even and odd vectors, respectively. This paper presents an analysis on the two vectors and new design methods for them. Also, based on the new design method, a signal flow graph of the proposed method is provided and compared with the CCI method in terms of complexity. To evaluate the proposed method, we conduct experiments and complexity comparison. The results indicate that the proposed interpolation method does not only outperform the existing method in terms of objective and subjective image quality but also requires less complexity.IEEE Transactions on Consumer Electronics 12/2009; · 1.09 Impact Factor
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ABSTRACT: In this paper, a three-dimensional (3D) image correlator using a fast computational integral imaging reconstruction (CIIR) method based on a pixel-to-pixel mapping is proposed. In order to implement the fast CIIR method, we replace the magnification process in the conventional CIIR by a pixel-to-pixel mapping. The proposed fast CIIR method reconstructs two sorts of plane images; a plane image whose quality is sufficient, and a dot pattern plane image insufficient to view. This property is very useful to enhance the performance of a CIIR-based image correlator. Thus, we apply the fast CIIR method to a CIIR-based image correlator. To show the feasibility of the proposed method, some preliminary experiments on both pattern correlation and computational cost are carried out, and the results are presented. Our experimental results indicate that the proposed image correlator is superior to the previous method in terms of both correlation performance and complexity.Optics Communications 01/2011; 284(21):5110-5114. · 1.44 Impact Factor
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ABSTRACT: This paper presents a new high-resolution computational integral imaging system employing a pickup with the axial movement of a lenslet array and a computation reconstruction algorithm with pixel-to-pixel mapping. In the proposed method, a lenslet array and its image sensor are moved together along the z-axis direction (or axial direction) and a series of elemental image arrays are obtained while moving. The elemental image arrays are then applied to pixel-to-pixel mapping without interpolation for the reconstruction of 3D slice images. Also, an analysis of the proposed reconstruction method is provided. To show the usefulness of the proposed method, experiments are conducted. The results indicate that the proposed method is superior to the existing method such as MALT in terms of image quality.Optics Express 04/2013; 21(7):8873-8. · 3.55 Impact Factor