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Simultaneous reconstruction of multiple depth images without off-focus points in integral imaging using a graphics

Faliu Yi, Jieun Lee, Inkyu Moon

|June 13, 2014

Summary

This study introduces a graphics processing unit (GPU) accelerated method for reconstructing 3D depth images in computational integral imaging. The GPU efficiently removes unwanted off-focus areas, improving 3D object analysis and computational speed.

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Area of Science:

Computer Vision
3D Reconstruction
Computational Imaging

Background:

Reconstructing 3D depth images using ray back-propagation in computational integral imaging is computationally intensive.
Reconstructed depth images contain focus and off-focus areas, where off-focus areas hinder 3D object analysis like classification and recognition.

Purpose of the Study:

To develop an efficient method for reconstructing multiple depth images and removing off-focus areas.
To leverage graphics processing units (GPUs) for accelerating the depth image reconstruction and off-focus point removal process.

Main Methods:

Utilized a graphics processing unit (GPU) for parallel processing to simultaneously reconstruct multiple depth images.
Employed a lookup table for shifted values and analyzed statistical variance of 3D points against 2D elemental images to classify focus and off-focus pixels.
Implemented parallel processing on the GPU for measuring focus and off-focus points.

Main Results:

The proposed GPU-accelerated method successfully reconstructs multiple depth images and removes off-focus points.
Significant improvement in computational speed was observed when using a GPU compared to a central processing unit (CPU).

Conclusions:

The GPU-based approach provides an efficient and accelerated solution for 3D depth image reconstruction and off-focus point removal in computational integral imaging.
This method enhances the accuracy of high-level 3D object analysis by eliminating noise introduced by off-focus areas.