Optically recorded analog holograms can reconstruct photorealistic three-dimensional (3D) images without the need for specialized eyewear. Computer-generated holograms (CGHs) are created by simulating the holographic recording process digitally rather than capturing them optically. Large-scale 3D still-image reconstruction with wide-viewing-zone can be achieved by mapping the amplitude or phase profiles of CGHs onto diffractive optical elements (DOEs), which modulate in-plane wavefront distribution using wavelength-scale surface structures. However, DOEs exhibit limited wavelength selectivity, interacting with a broad spectral range beyond their design wavelength. This results in reduced overall transmittance and typically requires three separate CGHs for full-color reconstruction. In this work, we present an "invisible holographic window," a transparent, surface-relief CGH patterned directly on glass via laser grayscale lithography. By encoding the real component of the interference pattern and reducing the phase-modulation range, the surface-relief structure transitions from deeply wrapped, jagged phase profiles to shallower and smoother sinusoidal phase profiles, which is associated with improved optical transmittance. Furthermore, a spatial-frequency-domain band-division multiplexing strategy is applied to support crosstalk-free, full-color 3D image reconstruction from a single transparent CGH, albeit with a viewing angle reduction. This platform generates photorealistic full-color 3D still-images in real space, offering new possibilities for transparent augmented-reality display interfaces such as those integrated into storefront windows, office glass partitions, and museum display cases. Our findings pave the way for advanced holographic display technologies and promise to accelerate research in the field.

Fresnel zone aperture (FZA) lens-less camera is a class of computational imaging systems that employs an FZA as a coded mask instead of an optical lens. FZA lens-less camera can perform fast deconvolution reconstruction and realize the re-focusing function. However, the reconstructed image’s spatial resolution is restricted by diffraction when using the conventional method derived from the geometrical optics model. In a previous study, we quantitatively analyzed the diffraction propagation between mask and sensor. Then we proposed a color-channel synthesis reconstruction method based on wave-optics theory. This study proposed a novel image reconstruction method without distorting the color information, comprehensively synthesizing two images captured with different mask-sensor distances to mitigate the diffraction influence and improve the image resolution. The numerical simulation and optical experiment results confirm that the proposed method can improve the spatial resolution to about two times that of the conventional method based on the geometrical optics model.

We present a system for generating and visualizing interactive 3D Augmented Reality tutorials based on 2D video input, which allows viewpoint control at runtime. Inspired by assembly planning, we analyze the input video using a 3D CAD model of the object to determine an assembly graph that encodes blocking relationships between parts. Using an assembly graph enables us to detect assembly steps that are otherwise difficult to extract from the video, and generally improves object detection and tracking by providing prior knowledge about movable parts. To avoid information loss, we combine the 3D animation with relevant parts of the 2D video so that we can show detailed manipulations and tool usage that cannot be easily extracted from the video. To further support user orientation, we visually align the 3D animation with the real-world object by using texture information from the input video. We developed a presentation system that uses commonly available hardware to make our results accessible for home use and demonstrate the effectiveness of our approach by comparing it to traditional video tutorials.

A light-field 3-D display (LFD) reproduces light rays or wavefront as if there are real objects, and a perfect 3-D display that satisfies all the depth cues in human vision becomes possible. By reproducing both horizontal and vertical parallaxes, i.e., full-parallax, an LFD reconstructs a real or virtual image in 3-D space. A rich 3-D experience can be provided to viewers, and moreover, multiple viewers can share the same reproduced 3-D image. A technology called holographic stereogram (HS) is the integration of ray-based and wavefront-based methods, and has been applied to LFD. Hardcopy HS has already been commercialized. The technology of HS is helpful also for an interactive LFD; a full-parallax LFD using a projector and a holographic screen that modulates the directions of light rays is based on the principle of HS. In this case, accurate registration between the projected image and the holographic screen is necessary, and it is achieved by projecting some test patterns and capturing them with a calibration camera. As a full-parallax LFD can reproduce real images in midair between the screen and viewers, it is possible to realize interactive 3-D touch interface. If a user touches the 3-D real image floating in the air, the reproduced light is scattered by the user's fingertip. The scattered light is detected by a color image sensor placed behind the holographic screen, and can be employed to detect the user's touch. In this system, the reproduced 3-D images and the 3-D touch detection are associated with each other, and thus, we do not have to worry about the complicated registration between them. The identification of the user's interaction is simple, because the color information of the 3-D image can be used for this purpose. Some experimental results of the 3-D touch-sensing display are introduced, and possible applications of this technology are discussed as well.

Recently an algorithm to separate fluorescent and reflective components from a hyperspectral image has been reported, in which the important task of spectral unmixing of multiple fluorescent components was left unresolved. In this paper, we present the algorithm to simultaneously separate those components and unmix fluorophores (SSUF: Simultaneous Separation and Unmixing of Fluorescent components) from a single hyperspectral image. Two variants are introduced for the cases when fluorophore spectra are known and unknown. Experimental results confirm the validity of the proposed method.

Many RGB digital cameras use a Bayer color filter array. In these cameras a full-resolution image is obtained from a mosaicked image by interpolation, and then the full resolution image is encoded. Similarly, mosaicked color filter arrays and interpolation methods for multispectral images have been proposed in recent years. The resulting full-resolution mutispectral images need to be encoded efficiently. This paper presents a new image compression method for one-shot multispectral imaging system. The proposed approach encodes the mosaicked multi-spectral image before interpolation. The decoder interpolates the image after decoding to reconstruct a full-resolution image. The simulation results show that the proposed method outperforms a conventional method, which encodes the full-resolution image after interpolation, at almost all bit rates. We also propose a heuristic color filter array design for our method that leads to lower bit rate.

In the compression technique of hyperspectral image (HSI), PSNR of the reconstructed image is usually used for evaluating the performance of the coding results. For the spectral analysis applications of HSI, it is also important to consider the error in the result of spectral analysis. In the vegetation analysis, for example, the distortion of the vegetation index should be considered in addition to the distortion in the spectral data. This paper presents a HSI compression considering the error of both vegetation index and spectral data. The proposed method separates a hyperspectral data into spectral data for vegetation index and residual data. Both of the data are encoded by using a seamless coding individually. By holding the spectral channels required for vegetation index in the head of the code-stream, a precise vegetation analysis can be done in a low bit rate. Additionally, by decoding the residual data, the spectral data can be reconstructed in low distortion.

This paper proposes a method for multispectral image compression that makes multispectral image applicable for both color reproduction and spectral image analysis. In the methods previously proposed for colorimetric error reduction, the weighting matrices were incorporated to Karhunen-Loeve transform (KLT) as the spectral transform for compression, which account for the color matching functions of human observer and the viewing illuminant. However, the improvement for color reproducibility is on the cost of spectral accuracy degradation. In this paper, we show the reduction of colorimetric error and the preservation of spectral accuracy is a tradeoff that can be controlled by adding a diagonal matrix to the weighting matrix of KLT. Experimental results using the proposed method integrated with JPEG2000 for a 16-band multispectral image show that spectral accuracy is improved without substantial colorimetric loss by the addition of diagonal matrix if the magnitude of the diagonal matrix is chosen appropriately.

Color reproduction systems using multispectral imaging techniques make it possible to accurately reproduce the color of the original object under various viewing illuminants. In this paper, a multispectral image compression method for high fidelity color reproduction is proposed in consideration of color degradation. In the proposed method, a spectral transform and a nonlinear quantization designed to reduce colorimetric error are combined with the discrete wavelet transform in JPEG2000. Through the experiments using some 16-band multispectral images, it is confirmed that the proposed method reduces the average and the maximum color differences in L*a*b* color space in comparison with the conventional methods.

In this paper we propose a method for unsupervised image segmentation, which is suitable for finding the features contained in medical images. The method is based on the hierarchical clustering method in multi-dimensional pattern vector space. We consider to change the size of pattern vectors adaptively to explore useful image features which can be used in medical diagnosis. We have tested our method on the simulation image, which is generated by the Markov Random Field (MRF) model, and the real medical images, photomicrographs of colon tumor, and its effectiveness is confirmed.

An effective method for an automated detection of long-term interval changes from a sequence of time-varying medical images is presented. We combine the pattern histogram method with an autoregressive model and novel classification algorithm to distinguish real abnormality variations from chaotic behaviors of background patterns without using an image registration technique. We conducted an experiment using 16 personal chest radiographs of pneumoconiosis acquired at the time span of 33 years. It is confirmed that the proposed method is able to detect suspicious regions and it is advantageous to conventional image comparison techniques particularly when the precise image registration is not available prior to subtraction.