Optical full-depth refocusing of 3-D objects based on subdivided-elemental images and local periodic δ-functions in integral imaging

Computational Integral Imaging Reconstruction via Elemental Image Blending without Normalization

This paper presents a novel computational integral imaging reconstruction (CIIR) method using elemental image blending to eliminate the normalization process in CIIR. Normalization is commonly used in CIIR to address uneven overlapping artifacts. By incorporating elemental image blending, we remove the normalization step in CIIR, leading to decreased memory consumption and computational time compared to those of existing techniques. We conducted a theoretical analysis of the impact of elemental image blending on a CIIR method using windowing techniques, and the results showed that the proposed method is superior to the standard CIIR method in terms of image quality. We also performed computer simulations and optical experiments to evaluate the proposed method. The experimental results showed that the proposed method enhances the image quality over that of the standard CIIR method, while also reducing memory usage and processing time.

Image Enhancement for Computational Integral Imaging Reconstruction via Four-Dimensional Image Structure

This paper describes the image enhancement of a computational integral imaging reconstruction method via reconstructing a four-dimensional (4-D) image structure. A computational reconstruction method for high-resolution three-dimensional (3-D) images is highly required in 3-D applications such as 3-D visualization and 3-D object recognition. To improve the visual quality of reconstructed images, we introduce an adjustable parameter to produce a group of 3-D images from a single elemental image array. The adjustable parameter controls overlapping in back projection with a transformation of cropping and translating elemental images. It turns out that the new parameter is an independent parameter from the reconstruction position to reconstruct a 4-D image structure with four axes of x, y, z, and k. The 4-D image structure of the proposed method provides more visual information than existing methods. Computer simulations and optical experiments are carried out to show the feasibility of the proposed method. The results indicate that our method enhances the image quality of 3-D images by providing a 4-D image structure with the adjustable parameter.

Computational integral imaging reconstruction of perspective and orthographic view images by common patches analysis

A novel method to computationally reconstruct perspective and orthographic view images with full resolution of a recording device from a single integral photograph is proposed. Firstly, a group of image slices that contain full yet redundant information to reconstruct the view image are generated, and the object surface is divided into pieces by the points that correspond to the centers of image slices. Secondly, the image slices that contribute to the pieces are extracted and redundant information embedded in them are figured out by common patches analysis. Finally, the view image is reconstructed by excluding the redundant information and resampling with maximum sampling rate. Each piece of the object surface is represented with 9 patches at most from 4 adjacent elemental images, and view images with high quality are reconstructed. Both simulations and experiments verify the validity of the method.

Human detection in occluded scenes through optically inspired multi-camera image fusion

In this paper, a novel approach for foreground extraction has been proposed based on a popular three-dimensional imaging technique in optics, called integral imaging. In this approach, multiple viewpoint images captured from a three-dimensional scene are used to extract range information of the scene and effectively extract an object or a person, even in the presence of heavy occlusion. The algorithm consists of two parts: depth estimation and reconstruction of the targeted object at the estimated depth distance. Further processing of the resulting reconstructed image can lead to the detection of a face or a pedestrian in the scene, which may not otherwise be detectable due to partial occlusion in each of the views. The validity of our approach has been demonstrated by experimental results in different scenarios.