Effect of different traversal schemes in integral image coding

Integral imaging (InIm) is a highly promising technique for the delivery of three-dimensional (3D) image content. During capturing, different views of an object are recorded as an array of elemental images (EIs), which form the integral image. High-resolution InIm requires sensors with increased resolution and produces huge amounts of highly correlated data. In an efficient encoding scheme for InIm compression both inter-EI and intra-EI correlations have to be properly exploited. We present an EI traversal scheme that maximizes the performance of InIm encoders by properly rearranging EIs to increase the intra-EI correlation of jointly coded EIs. This technique can be used to augment performance of both InIm specific and properly adapted general use encoder setups, used in InIm compression. An objective quality metric is also introduced for evaluating the effects of different traversal schemes on the encoder performance.     

Real-time three-dimensional object recognition with multiple perspectives imaging

A novel system for recognizing three-dimensional (3D) objects by use of multiple perspectives imaging is proposed. A 3D object under incoherent illumination is projected into an array of two-dimensional (2D) elemental images by use of a microlens array. Each elemental 2D image corresponds to a different perspective of the 3D object. Multiple perspectives imaging based on integral photography has been used for 3D display. In this way, the whole set of 2D elemental images records 3D information about the input object. After an optical incoherent-to-coherent conversion, an optical processor is employed to perform the correlation between the input and the reference 3D objects. Use of micro-optics allows us to process the 3D information in real time and with a compact optical system. To the best of our knowledge this 3D processor is the first to apply the principle of integral photography to 3D image recognition. We present experimental results obtained with both a digital and an optical implementation of the system. We also show that the system can recognize a slightly out-of-plane rotated 3D object.     

Efficient storage and transmission of ladar imagery

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression.     

Depth extraction of three-dimensional objects using block matching for slice images in synthetic aperture integral imaging

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.     

Hierarchical prediction structure for subimage coding and multithreaded parallel implementation in integral imaging

We are concerned with the coding of subimage-transformed elemental images to solve the problems of data transmission and storage in three-dimensional (3D) integral imaging in this paper. First, we use the subimage transform for preprocessing of the elemental image array (EIA). Because of the similarity of correlation distributions between the subimage array (SIA) and multiview video, we present a hierarchical prediction structure for SIA coding based on the hierarchical B picture (HBP) structure for multiview video coding. Moreover, we design a multithreaded parallel implementation for the proposed structure according to inter-row prediction dependencies. Experiments are performed on both EIAs and SIAs. The results show that employing the same coding strategy, the proposed parallel implemented HBP scheme achieves not only higher image quality and better 3D effect but also lower coding delay at low bit rates compared with the previously reported Hilbert-curve-based scheme.     

Microlens arrays for integral imaging system

When designing a system capable of capturing and displaying 3D moving images in real time by the integral imaging (II) method, one challenge is to eliminate pseudoscopic images. To overcome this problem, we propose a simple system with an array of three convex lenses. First, the lateral magnification of the elemental optics and the expansion of an elemental image is described by geometrical optics, confirming that the elemental optics satisfies the conditions under which pseudoscopic images can be avoided. In using the II method, adjacent elemental images must not overlap, a condition also satisfied by the proposed optical system. Next, an experiment carried out to acquire and display 3D images is described. The real-time system we have constructed comprises an elemental optics array with 54 H x 59 V elements, a CCD camera to capture a group of elemental images created by the lens array, and a liquid crystal panel to display these images. The results of the experiment confirm that the system produces orthoscopic images in real time, and thus is effective for real-time application of the II method.     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013     

3D LiDAR point cloud image codec based on Tensor

ABSTRACT

This paper proposes a new and efficient codec called 3D Light Detection and Ranging (LiDAR) point cloud coding based on tensor (LPCT) concepts. By combining the techniques of Statistical Subspace Outlier Detection and Logarithmic Transformation, LPCT effectively makes the unreliable points imperceptible and diminishes the spatial coefficient ranges. LPCT is applied to achieve the perfect encoding and decoding performances by using tensor. The iterative compression method is introduced to immensely reduce the dimension of a higher-order point cloud data. Experimental results reveal that the proposed LPCT yields a better independent compression ratio (CR) and impressive quality of a decompressed image than the existing well-liked compression approaches, namely 7-Zip and WinRAR. This work proves that the proposed lossless LPCT algorithm compresses the spatial information of various size point cloud images into six bytes and produces better Hausdorff peak signal-to-noise ratio (PSNR) for the shortest distance point cloud image.     

3D photon counting integral imaging with unknown sensor positions

Photon counting techniques have been introduced with integral imaging for three-dimensional (3D) imaging applications. The previous reports in this area assumed a priori knowledge of exact sensor positions for 3D image reconstruction, which may be difficult to satisfy in certain applications. In this paper, we extend the photon counting 3D imaging system to situations where sensor positions are unknown. To estimate sensor positions in photon counting integral imaging, scene details of photon counting images are needed for image correspondences matching. Therefore, an iterative method based on the total variation maximum a posteriori expectation maximization (MAP-EM) algorithm is used to restore photon counting images. Experimental results are presented to show the feasibility of the method. To the best of our knowledge, this is the first report on 3D photon counting integral imaging with unknown sensor positions.     

Analysis of denoising filters on MRI brain images

The magnetic resonance imaging (MRI) modality is an effective tool in the diagnosis of the brain. These MR images are introduced with noise during acquisition which reduces the image quality and limits the accuracy in diagnosis. Elimination of noise in medical images is an important task in preprocessing and there exist different methods to eliminate noise in medical images. In this article, different denoising algorithms such as nonlocal means, principal component analysis, bilateral, and spatially adaptive nonlocal means (SANLM) filters are studied to eliminate noise in MR. Comparative analysis of these techniques have been with help of various metrics such as signal‐to‐noise ratio, peak signal‐to‐noise ratio (PSNR), mean squared error, root mean squared error, and structure similarity (SSIM). This comparative study shows that the SANLM denoising filter gives the best performance in terms of better PSNR and SSIM in visual interpretation. It also helps in clinical diagnosis of the brain.     

3D-image restoration technique using Genetic Algorithm to solve blurring problems of images

Most images may not be sharp and clear due to various reasons like noise interference and is said to be in a blurred condition. Image de-blurring is fundamental in making pictures sharp and useful. Normally, along with the input blurred image, Point Spread Function (PSF) of the original image is required for the process of restoration and de-blurring. In this paper, we introduce a technique for image restoration by Richardson–Lucy algorithm where the optimised PSF is generated by the use of Genetic Algorithm (GA). Use of optimised PSF ensures that our proposed technique does not need the original image for the de-blurring purpose and can be greatly beneficial in the real time scenario cases. The dataset used for the evaluation of the proposed technique are real 3D images and the evaluation metrics used are peak signal-to-noise ratio (PSNR), Second-Derivative like Measure of Enhancement (SDME) and mean squared error (MSE). The technique is compared with existing techniques such as de-convolution method, regularisation filter, Wiener filter and Richardson–Lucy algorithm. From the results, we can observe that our proposed technique has achieved higher PSNR and SDME values and lower MSE values when compared with other techniques. We have achieved average PSNR of 70·94, SDME of 71·46 and MSE of 0·0063. The values obtained show the superior performance of the proposed technique.     

Depth extraction by using the correlation of the periodic function with an elemental image in integral imaging

We propose a depth extraction method by using the correlation between an elemental image and a periodic function in computational integral imaging. Because each elemental image corresponds to a different perspective of the three-dimensional (3-D) object, an elemental image is regarded as the sum of the periodic spatial frequencies depending on the depth of a 3-D object. In this regard, we analyze the property of correlation between the same periodic functions and vice versa. To show the feasibility of the proposed method, we carried out our experiment and presented the results.     

Magnification of 3D reconstructed images in integral imaging using an intermediate-view reconstruction technique

A new integral imaging (II) system that can magnify 3D reconstructed images by employing an intermediate-view reconstruction technique (IVRT) is proposed in which the number of the elemental images obtained from a one-step pickup process can be computationally increased by use of an IVRT without mechanical movement and a long multistep pickup process. To show the feasibility of the proposed II system, some optical experiments on the magnification of 3D reconstructed images with a real 3D object have been carried out and results are presented.     

基于位平面可预测性的无损图像压缩研究

提出了一种基于嵌入式位平面的静止连续色调图像的无损图像压缩方法：通过将1幅图像分割成两类位平面（基础层和增强层）使得该图像具有了位平面的可测量性，并且通过利用平面与平面以及每个平面中各像素之间的相关性减少冗余，从而获得优秀的压缩性能；与其他压缩算法的比较表明，基于嵌入式位平面的无损图像压缩算法由于具有位平面可测量性而体现了巨大的优越性。     

一种基于嵌入式位平面的无损图像压缩方法

提出了一种基于嵌入式位平面(bit-plane)的静止连续色调图像的无损图像压缩方法。该方法通过将一幅图像分割成两类位平面(基础层和增强层)使得该图像具有了位平面的可测量性(levelscalability),并且通过利用平面与平面以及每个平面中各像素之间的相关性减少冗余,从而获得优秀的压缩性能。通过大量的与其他压缩算法的比较,可以得出:基于嵌入式位平面的无损图像压缩算法仅在压缩效率上稍逊于其他不可测量(non-scalable)无损压缩算法,但却由于它具有位平面可测量性而体现了巨大的优越性。     

Enhanced three-dimensional discrete cosine transform based compression method for integral images by adaptive three-dimensional block construction

In this paper, we propose an efficient compression method for integral images based on three-dimensional discrete cosine transform (3D-DCT). Even though the existing 3D-DCT based techniques are efficient, they may not be optimized to the characteristics of integral images, such as applying a fixed size block construction and a fixed scanning in placing 2D blocks to construct a 3D block. Therefore, we propose a variable size block construction and a scanning method adaptive to characteristics of integral images, which are realized by adaptive 3D block modes. Experimental results show that the proposed method gives significant improvement in coding efficiency. In particular, at the high bit rates, the proposed method is more improved, since overhead bits for signaling of the 3D block modes take a smaller part of the total bits.     

Significance of pre-processing and its impact on lossless hyperspectral image compression

The primitive aspect of hyperspectral imagery is its inherent spatial and spectral correlation. This correlation is exploited by subjecting the imaging cube to compression. A new approach to accomplish lossless hyperspectral image compression has been proposed. The imaging cube is subjected to pre-processing stage prior to entropy coding. Pre-processing stage comprises band normalization, ordering of bands followed by image scanning. A new sorting technique entitled Greedy Heap Sorting is suggested. The proposed strategy yields an average compression ratio (CR) of 4.93 and average bits per pixel (bpp) of 3.08. The proficiency of the system is on par with the existing contemporary algorithms for lossless hyperspectral image compression in terms of CR, bpp and reduced complexity.     

Wide-viewing integral three-dimensional imaging by use of orthogonal polarization switching

A wide-viewing integral three-dimesional (3D) imaging system that adopts orthogonal polarization switching is proposed and demonstrated. In our scheme,the polarizing sheet attached to the lens array and the orthogonal polarization switching of the elemental image array perform elemental lens switching. The experimental results document that the viewing angle becomes remarkably wider than that of the conventional method. The distinguishing feature of our system is that it requires no mechanical moving part. In addition, because a commercially available polarization shutter screen is used for electrical switching, it is easy to implement this as a practical system. We believe that the proposed method facilitates the practical use of this wide-viewing integral 3D imaging system.     

Depth extraction of three-dimensional objects in space by the computational integral imaging reconstruction technique

A novel approach to extract the depth data of 3D objects in space by using the computational integral imaging reconstruction (CIIR) technique is proposed. With elemental images of 3D objects captured by the CCD camera through a pinhole array, depth-dependent object images can be reconstructed on the output plane by the CIIR technique. Only the images reconstructed on the output planes where 3D objects were located are clearly focused; so the depth data of 3D objects in space can be extracted by discriminating these focused output images from the others by using an image separation technique. A feasibility test of the proposed CIIR-based depth extraction method is carried out, and its results are discussed as well.     

Effect of illumination in an integral imaging system with large depth of focus

We present the characteristics of integral imaging systems with large depth of focus (DOF) by use of two kinds of illumination: plane illumination and diffusing illumination. For each system, we perform ray analysis based on ray optics. To check the visual quality through optical experiments, we use an average image of observed images picked up at various positions within a large DOF. The synthesized elemental images for a three-dimensional (3-D) object with two character patterns were displayed in an optical system and its reconstruction experiments are performed. Experimental results show that use of diffusing illumination can improve visual quality of reconstruction 3-D images in depth-priority integral imaging.