Stereoscopic images and videos can lead to serious adverse effects on human visual perception. The phenomenon of visual discomfort depends on various influencing factors such as the arrangement of the display system, the image quality and the design of 3D effects. Real-time depth adaptations that reduce the extent of visual discomfort require computationally efficient prediction models. This article analyzes optimal combinations of image features of state-of-the-art models in terms of prediction accuracy and computational efficiency. In addition, a fast-to-compute disparity contrast feature based on Haralick contrast is introduced in this context. It turns out that the computational complexity can be reduced by restricting the number of features without loss of prediction accuracy. A Pareto-front analysis shows which features are more likely to be part of optimal combinations. It is interesting to observe that the introduced disparity contrast feature is part of combinations that are optimal in terms of both computational efficiency and accuracy. This means that state-of-the-art prediction models can be improved by means of the introduced disparity contrast feature. The analysis relies on statistical evaluations based on publicly available assessment data.

This article describes a high resolution aerial 3D display using a time-division multiplexing directional backlight. In this system, an aerial real image is generated with a pair of large convex lenses. The directional backlight is controlled based on the detected face position so that binocular stereoscopy may be maintained for a moving observer. By use of the directional backlight, the proposed system attains autostereoscopy without any moving parts. A wide viewing zone is realized by placing a large aperture convex lens between the backlight and the LCD panel. With the advantage of time-division multiplexing, a high resolution 3D image is presented to the viewer.

With the miniaturization of precision machine components, the measurement of data at a high resolution is important for product inspection. Microscopy measurement techniques have been improved to a high resolution so as to satisfy the need. However, since the range of high-resolution measurement is narrow, it is difficult to observe the entire structure of the target at a time. If it is possible to unify many measurement data taken under different conditions into one dataset while keeping the positional relationships between the data, one can effectively observe details and understand the entire structure. Hence, we develop an image processing system that synthesizes measurement data with different resolutions and displays both the entire structure and fine details of the observed object simultaneously. In order to unify the measurement data, we propose two image synthesis methods based on feature points and edge information. This article also describes the details of the system which can generate multi-resolution models by using the proposed methods and allows users to arbitrarily vary the display area and magnification while observing the entire structure.

There exists an increasing interest in reproducing surface appearances by means of digital printing, thereby expanding the reproduction from only color to reproduction of texture and reflection properties such as the BRDF. Previous research has focused on different ways to obtain control of the optical characteristics of digital prints, either by introducing additional inks or by modulating the surface texture on a macro level. The authors propose to utilize the different parameters of a printing system, which influence aspects such as the ink deposition and drying time, to impact the roughness of the print surface on a micro level. By investigating relationships between optical and geometric characteristics of printed surfaces, we study the hypothesis that both surface characteristics can be estimated from a single roughness parameter. From these findings, we propose a workflow to control reflection properties (including color) of a printed surface by determining ink coverage values and print parameters.

With the introduction of compressed sensing (CS) theory, investigation into exploiting sparseness and optimizing compressive sensing performance has ensued. Compressed sensing is highly applicable to images, which naturally have sparse representations. Improvements in the area of image denoising have resulted from the combination of highly-directional transforms with shrinkage and thresholding techniques along with imposition of a model to account for statistical properties of images. Using this approach, statistical modeling of dependencies in the transform domain is incorporated into high-performance and efficient state-of-the-art CS image reconstruction algorithms with highly-directional transforms incorporating redundancy and bivariate shrinkage and thresholding to further refine image reconstruction performance improvements. Additionally, hierarchical structural dependency modeling is incorporated to account for parent–child coefficient relationships. These techniques exploit hierarchical structure and multiscale subbands of frequencies and orientation, exploiting dependencies across and within scales. Additionally, these techniques are incorporated with minimal additional CPU execution time into block-based CS (BCS) algorithms, which are known for their efficient and fast computation time. Experimental results show increased refinements of image reconstruction performance over current state-of-the-art image reconstruction algorithms, particularly at the higher CS ratios (lower sampling rates) of interest in compressed sensing.

This article introduces a novel algorithm to learn optimal incident illumination for material classification using spectral bidirectional reflectance distribution function (BRDF) images. The method performs a joint selection of incident angle and spectral band in two steps: (1) clustering and selecting incident angles using statistics on the spectral BRDF images for a specific material, and (2) searching for the optimal angles and spectral bands that maximize material discriminability, which we measure in classification performance. The benefits of reducing the number of incident illumination angles include improving material classification, reducing computational time and storage, and allowing for a less cumbersome and potentially mobile imaging system. The authors show that their approach provides comparable material classification performance when using a reduced number of incident illuminations as compared with when using a larger number. They also compare their approach with prior work.