Three-dimensional image correlator using fast computational integral imaging reconstruction method based on pixel-to-pixel mapping

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.     

基于立体像素匹配的图像重构技术研究

为了解决目前全景成像技术中分辨率低的问题,提出了一种新的基于3D场景立体像素光线映射的全景图像计算机重构技术.在全景成像技术中,3D场景的每个立体像素点经全景成像系统的编码系统分别映射在一定区域的多个体元素图像的不同像素点上.在计算机重构全景图像时,根据逆光学路径原理,提出了从立体像素映射到的体元素图像区域中提取对应立体像素的多个2D像素点来重构全景图像,使重构的全景图像最大分辨率可达到传统成像方法图像分辨率的N倍（N为映射区域面积）.提出的立体像素的匹配技术大大提高了重构的计算机全景图像分辨率.     

利用匹配区域的纹理特征改善重构三维图像的视觉质量

研究了计算机重构三维图像时分辨率低的问题,提出一种改善3D计算机全景重构图像的视觉质量的方法,该方法利用3D空间的物体部分在每个元素图像中形成的匹配区域的纹理特征,从两个相邻的元素图像中的匹配区域提取出多个像素,经过加权计算重构出相应的图像区域.该方法与传统的计算机重构方法相比,提高了图像分辨率,同时也消除了从每个元素图像中提取多个像素直接重构图像时存在的"像素块"效应,改善了重构图像的视觉质量.     

A 3D image encryption technique using computer-generated integral imaging and cellular automata transform

A novel three-dimensional (3D) image encryption approach by using the computer-generated integral imaging and cellular automata transform (CAT) is proposed, in which, the two-dimensional (2D) elemental image array (EIA) digitally recorded by light rays coming from the 3D image is mapped inversely through the virtual pinhole array according to the ray-tracing theory. Next, the encrypted image is generated by using the 2D CAT scrambling transform for the 2D EIA. The reconstructed process is carried out by using the modified computational integral-imaging reconstruction (CIIR) technique; the depth-dependent plane images are reconstructed on the output plane. The reconstructed 3D image quality of the proposed scheme can be greatly improved, because the proposed encryption scheme carries out in a computer which can avoid the light diffraction caused by optical device CIIR, and solves blur problem caused by CIIR by using the pixel-averaging algorithm. Furthermore, the CAT-based encryption algorithm is an error-free encryption method; CAT as an orthogonal transformation offers considerable simplicity in the calculation of the transform coefficient, that is, it can improve the quality of the reconstructed image by reducing energy loss compared with the traditional complicated transform process. To show the effectiveness of the proposed scheme, we perform computational experiments. Experimental results show that the proposed scheme outperforms conventional encryption methods.     

Computational integral imaging reconstruction scheme of far 3D objects by additional use of an imaging lens

In this paper, we propose a novel performance-enhanced computational integral imaging reconstruction (CIIR) scheme by additional use of an imaging lens. In the proposed scheme, elemental images can be obtained by using a simultaneous pickup scheme of far three-dimensional (3D) objects from the lenslet array in both real and virtual image fields. And additional imaging lens produces an image shift effect of 3D objects located far away from the lenslet array and improve the visual quality of reconstructed images in CIIR by overcoming limitation of pickup range in integral imaging. To show the usefulness of the proposed system, some experiments are carried out for real 3D objects and its results are presented.     

3D image reconstruction with a controllable overlapping number of elemental images in computational integral imaging

In this Letter, we propose a three-dimensional(3D) image reconstruction method with a controllable overlapping number of elemental images in computational integral imaging. The proposed method can control the overlapping number of pixels coming from the elemental images by using the subpixel distance based on ray optics between a 3D object and an image sensor. The use of a controllable overlapping number enables us to provide an improved 3D image visualization by controlling the inter-pixel interference within the reconstructed pixels.To find the optimal overlapping number, we simulate the pickup and reconstruction processes and utilize the numerical reconstruction results using a peak signal-to-noise ratio(PSNR) metric. To demonstrate the feasibility of our work in optical experiments, we carry out the preliminary experiments and present the results.     

Modified computational integral imaging-based double image encryption using fractional Fourier transform

In this paper, we propose an image encryption technique to simultaneously encrypt double or multiple images into one encrypted image using computational integral imaging (CII) and fractional Fourier transform (FrFT). In the encryption, each of the input plane images are located at different positions along a pickup plane, and simultaneously recorded in the form of an elemental image array (EIA) through a lenslet array. The recorded EIA to be encrypted is multiplied by FrFT with two different fractional orders. In order to mitigate the drawbacks of occlusion noise in computational integral imaging reconstruction (CIIR), the plane images can be reconstructed using a modified CIIR technique. To further improve the solution of the reconstructed plane images, a block matching algorithm is also introduced. Numerical simulation results verify the feasibility and effectiveness of the proposed method.     

Regeneration of elemental images in integral imaging for occluded objects using a plenoptic camera

In this Letter, we propose an elemental image regeneration method of three-dimensional(3D) integral imaging for occluded objects using a plenoptic camera. In conventional occlusion removal techniques, the information of the occlusion layers may be lost. Thus, elemental images have cracked parts, so the visual quality of the reconstructed 3D image is degraded. However, these cracked parts can be interpolated from adjacent elemental images. Therefore, in this Letter, we try to improve the visual quality of reconstructed 3D images by interpolating and regenerating virtual elemental images with adjacent elemental images after removing the occlusion layers. To prove our proposed method, we carry out optical experiments and calculate performance metrics such as the mean square error(MSE) and the peak signal-to-noise ratio(PSNR).     

基于视差信息的三维图像的计算机重构*

为了解决全景成像技术中观察者位于观察区域之外看到的图像会存在失真的问题,提出了一种基于视差信息的计算机重构3D视图技术.利用3D场景中的物体点经过不同微透镜在元素图像中记录的视差信息,根据光学路径分析,对重构视图中的失真部分用其它元素图像中存在的同一物体点的匹配像素进行替代,从而得到无失真的3D视图.该技术能够在更宽的观察区域内产生3D图像.     

基于视差信息的三维图像的计算机重构

为了解决全景成像技术中观察者位于观察区域之外看到的图像会存在失真的问题,提出了一种基于视差信息的计算机重构3D视图技术.利用3D场景中的物体点经过不同微透镜在元素图像中记录的视差信息,根据光学路径分析,对重构视图中的失真部分用其它元素图像中存在的同一物体点的匹配像素进行替代,从而得到无失真的3D视图.该技术能够在更宽的观察区域内产生3D图像.     

Optical 3D watermark based digital image watermarking for telemedicine

Region of interest (ROI) of a medical image is an area including important diagnostic information and must be stored without any distortion. This algorithm for application of watermarking technique for non-ROI of the medical image preserving ROI. The paper presents a 3D watermark based medical image watermarking scheme. In this paper, a 3D watermark object is first decomposed into 2D elemental image array (EIA) by a lenslet array, and then the 2D elemental image array data is embedded into the host image. The watermark extraction process is an inverse process of embedding. The extracted EIA through the computational integral imaging reconstruction (CIIR) technique, the 3D watermark can be reconstructed. Because the EIA is composed of a number of elemental images possesses their own perspectives of a 3D watermark object. Even though the embedded watermark data badly damaged, the 3D virtual watermark can be successfully reconstructed. Furthermore, using CAT with various rule number parameters, it is possible to get many channels for embedding. So our method can recover the weak point having only one transform plane in traditional watermarking methods. The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results.     

A novel three-dimensional digital watermarking scheme basing on integral imaging

A novel integral imaging-based three-dimensional (3D) digital watermarking scheme is presented. In the proposed method, an elemental image array (EIA) obtained by recording the rays coming from a 3D object through a pinhole array in the integral imaging system is employed as a new 3D watermark. The EIA is composed of a number of small elemental images having their own perspectives of a 3D object, and from this recorded EIA various depth-dependent 3D object images can be reconstructed by using the computational integral imaging reconstruction (CIIR) technique. This 3D property of the EIA watermark can make a robust reconstruction of the watermark image available even though there are some data losses in the embedded watermark by attacks. To show the robustness of the proposed scheme against attacks, some experiments are carried out and the results are discussed.     

Resolution-enhanced 3D image correlator using computationally reconstructed integral images

In this paper, a novel resolution-enhanced three-dimensional (3D) image correlator using the computationally reconstructed integral images is proposed in order to extract target object’s 3D location data in a scene. Elemental images of the reference and target objects are picked up by lenslet arrays and using these elemental images, reference and target plane images are reconstructed on the output plane by means of a modified computational integral imaging reconstruction technique. Then, through cross-correlations between the reconstructed reference and the target plane images, 3D location data of the target object can be extracted from the correlation outputs. With the purpose of showing the feasibility of the proposed method, some computational and optical experiments on the target objects in space are carried out and the results are presented.     

Depth-controlled reconstruction of 3D integral image using synthesized intermediate sub-images

In this paper, we propose a method that controls the depth of the three-dimensional (3D) object existing over the depth-of-focus in integral imaging. The depth control method is performed only in a computer by synthesizing the intermediate sub-images between original sub-images obtained by transforming the captured elemental images. In the reconstruction process, we can obtain reconstructed 3D images with the better image quality within depth-of-focus than that reconstructed over the depth-of-focus. To demonstrate the feasibility of our method, optical and computational experiments are carried out and its results are presented.     

Three-dimensional visualization of objects in scattering medium using integral imaging and spectral analysis

This paper presents a three-dimensional visualization method of 3D objects in a scattering medium. The proposed method employs integral imaging and spectral analysis to improve the visual quality of 3D images. The images observed from 3D objects in the scattering medium such as turbid water suffer from image degradation due to scattering. The main reason is that the observed image signal is very weak compared with the scattering signal. Common image enhancement techniques including histogram equalization and contrast enhancement works improperly to overcome the problem. Thus, integral imaging that enables to integrate the weak signals from multiple images was discussed to improve image quality. In this paper, we apply spectral analysis to an integral imaging system such as the computational integral imaging reconstruction. Also, we introduce a signal model with a visibility parameter to analyze the scattering signal. The proposed method based on spectral analysis efficiently estimates the original signal and it is applied to elemental images. The visibility-enhanced elemental images are then used to reconstruct 3D images using a computational integral imaging reconstruction algorithm. To evaluate the proposed method, we perform the optical experiments for 3D objects in turbid water. The experimental results indicate that the proposed method outperforms the existing methods.     

Directional variation adaptive image resolution enhancement

The goal of image interpolation is to produce a high-resolution image from its low-resolution counterpart. It has significant applications in video sensor network, where the resolution of images usually needs to be enhanced at the end user due to the limited transmission bandwidth. The key challenge of image interpolation is to preserve the edge structure of the image. In this paper, a new image interpolation approach is proposed to adaptively adjust the interpolation according to the directional variations of images. More specifically, at each pixel position to be interpolated, its neighboring pixels are projected onto 1D direction according to a number of proposed patterns. Then the direction, of which the variation is smallest, is chosen as the direction to perform image interpolation. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms.     

Three-dimensional imaging and visualization of partially occluded objects using axially distributed stereo image sensing

In this Letter, we propose a multiperspective three-dimensional (3D) imaging system using axially distributed stereo image sensing. In this proposed method, the stereo camera is translated along its optical axis and multiple axial elemental image pairs for a 3D scene are collected. The captured elemental images are reconstructed in 3D using a computational reconstruction algorithm based on ray back-projection. The proposed method is applied to partially occluded object visualization. Optical experiments are performed to verify the approach.     

Visually weighted reconstruction of compressive sensing MRI

Compressive sensing (CS) enables the reconstruction of a magnetic resonance (MR) image from undersampled data in k-space with relatively low-quality distortion when compared to the original image. In addition, CS allows the scan time to be significantly reduced. Along with a reduction in the computational overhead, we investigate an effective way to improve visual quality through the use of a weighted optimization algorithm for reconstruction after variable density random undersampling in the phase encoding direction over k-space. In contrast to conventional magnetic resonance imaging (MRI) reconstruction methods, the visual weight, in particular, the region of interest (ROI), is investigated here for quality improvement. In addition, we employ a wavelet transform to analyze the reconstructed image in the space domain and fully utilize data sparsity over the spatial and frequency domains. The visual weight is constructed by reflecting the perceptual characteristics of the human visual system (HVS), and then applied to ?₁ norm minimization, which gives priority to each coefficient during the reconstruction process. Using objective quality assessment metrics, it was found that an image reconstructed using the visual weight has higher local and global quality than those processed by conventional methods.     

Dictionary learning based sinogram inpainting for CT sparse reconstruction

In CT (computed tomography), reconstruction from undersampling projection data is often ill-posed and suffers from severe artifact in the reconstructed images. To overcome this problem, this paper proposes a sinogram inpainting method based on recently rising sparse representation technology. In this approach, a dictionary learning based inpainting is used to estimate the missing projection data. The final image can be reconstructed by the analytic filtered back projection (FBP) reconstruction. We conduct experiments using both simulated and real phantom data. Compared to the comparative interpolation method, visual and numerical results validate the clinical potential of the proposed method.     

Edge adaptive image resolution enhancement in video sensor network

Video sensor network usually uses fairly low-resolution images due to the limited transmission bandwidth in transmitting images. It is potential to enhance the captured low-resolution images using image resolution enhancement technique that is able to produce a high-resolution image from its low-resolution counterpart. The key challenge of image resolution enhancement is to preserve the edge structure in images. In this paper, a new image resolution enhancement approach is proposed to estimate the intensity of the unknown pixel using a bilateral weighted average of that of its neighboring pixels. More specifically, the neighboring pixels with nearer distance have larger contributions. Furthermore, the neighboring pixels belonging to direction with smaller variation have larger contributions. Experimental results are provided to show that the proposed approach outperforms several conventional edge-directed image interpolation algorithms. Furthermore, the proposed approach yields low computational complexity; it is potential for real-time implementation.