The human visual system is capable of adapting across a very wide dynamic range of luminance levels; values up to 14 log units have been reported. However, when the bright and dark areas of a scene are presented simultaneously to an observer, the bright stimulus produces significant glare in the visual system and prevents full adaptation to the dark areas, impairing the visual capability to discriminate details in the dark areas and limiting simultaneous dynamic range. Therefore, this simultaneous dynamic range will be much smaller, due to such impairment, than the successive dynamic range measurement across various levels of steady-state adaptation. Previous indirect derivations of simultaneous dynamic range have suggested between 2 and 3.5 log units. Most recently, Kunkel and Reinhard reported a value of 3.7 log units as an estimation of simultaneous dynamic range, but it was not measured directly. In this study, simultaneous dynamic range was measured directly through a psychophysical experiment. It was found that the simultaneous dynamic range is a bright-stimulus-luminance dependent value. A maximum simultaneous dynamic range was found to be approximately 3.3 log units. Based on the experimental data, a descriptive log-linear model and a nonlinear model were proposed to predict the simultaneous dynamic range as a function of stimulus size with bright-stimulus luminance-level dependent parameters. Furthermore, the effect of spatial frequency in the adapting pattern on the simultaneous dynamic range was explored. A log parabola function, representing a traditional Contrast Sensitivity Function (CSF), fitted the simultaneous dynamic range data well.

Image captioning generates text that describes scenes from input images. It has been developed for high-quality images taken in clear weather. However, in bad weather conditions, such as heavy rain, snow, and dense fog, poor visibility as a result of rain streaks, rain accumulation, and snowflakes causes a serious degradation of image quality. This hinders the extraction of useful visual features and results in deteriorated image captioning performance. To address practical issues, this study introduces a new encoder for captioning heavy rain images. The central idea is to transform output features extracted from heavy rain input images into semantic visual features associated with words and sentence context. To achieve this, a target encoder is initially trained in an encoder–decoder framework to associate visual features with semantic words. Subsequently, the objects in a heavy rain image are rendered visible by using an initial reconstruction subnetwork (IRS) based on a heavy rain model. The IRS is then combined with another semantic visual feature matching subnetwork (SVFMS) to match the output features of the IRS with the semantic visual features of the pretrained target encoder. The proposed encoder is based on the joint learning of the IRS and SVFMS. It is trained in an end-to-end manner, and then connected to the pretrained decoder for image captioning. It is experimentally demonstrated that the proposed encoder can generate semantic visual features associated with words even from heavy rain images, thereby increasing the accuracy of the generated captions.

We have developed a system to measure both the optical properties of facial skin and the three-dimensional shape of the face. To measure the three-dimensional facial shape, our system uses a light-field camera to provide a focused image and a depth image simultaneously. The light source uses a projector that produces a high-frequency binary illumination pattern to separate the subsurface scattering and surface reflections from the facial skin. Using a dichromatic reflection model, the surface reflection image of the skin can be separated further into a specular reflection component and a diffuse reflection component. Verification using physically controlled objects showed that the separation of the optical properties by the system correlated with the subsurface scattering, specular reflection, or diffuse reflection characteristics of each object. The method presented here opens new possibilities in cosmetology and skin pharmacology for measurement of the skin’s gloss and absorption kinetics and the pharmacodynamics of various external agents.

Material appearance is a perceptual phenomenon that the brain interprets from the retinal image. Though, it is not easy to analyze what features of optical images are effectively related to the stimulus inside the visual cortex. For this reason, an intuitive or heuristic approach has been taken to simulate the material appearance. The simulation results are expected to drive innovation for not only traditional craft or plastic arts industry but also more realistic picture displays on 4K/8K HDTV and Virtual Reality or Computer Graphics. Optical surface property of material is modeled by BRDF (Bidirectional Reflectance Distribution Function). Specular S and Diffusion D components are responsible for the “glossiness” and “texture” and are used to emphasize the material appearance by simply adjusting the mixing ratio. This study introduces the following two key models to emphasize the material appearance of a given image without using such measuring means as BRDF and discusses how they work individually and cooperatively. (1) α-based Dehazing model to emphasize clarity, wetness, gloss. (2) β-based Contrast model to emphasize texture, roughness.

The appearance of color stimuli with luminance levels beyond the diffuse white is gaining importance due to the popularity of high dynamic range (HDR) displays. Past work on color appearance of stimuli, color appearance models, and uniform color spaces mainly focused on the stimuli with luminance levels below the diffuse white, which were produced using surface color samples or conventional standard dynamic range (SDR) displays. In this study, we focused on the perception of white appearance for stimuli with luminance beyond the diffuse white. Human observers adjusted the color appearance of a stimulus to the whitest under different adapting conditions, including a dark condition and 12 illuminated conditions. It was found that the chromaticities for producing the white appearance under the dark condition were generally similar to those under the 6500 K conditions, regardless of the adapting luminance levels. In comparison to a recent study focusing on the stimuli with luminance below the diffuse white, the perception of white under the conditions with the adapting CCT levels of 2700, 3500, and 5000 K was significantly affected by the lightness level of the stimulus, which cannot be accurately characterized by CAM02-UCS. The results can be used for reproducing white appearance for highlights in HDR scenes. Further investigations on uniform color spaces for characterizing stimuli with luminance beyond the diffuse white are urgently needed for processing and displaying HDR images.

Pigment classification of paintings is considered an important task in the field of cultural heritage. It helps to analyze the object and to know its historical value. This information is also essential for curators and conservators. Hyperspectral imaging technology has been used for pigment characterization for many years and has potential in its scientific analysis. Despite its advantages, there are several challenges linked with hyperspectral image acquisition. The quality of such acquired hyperspectral data can be influenced by different parameters such as focus, signal-to-noise ratio, illumination geometry, etc. Among several, we investigated the effect of four key parameters, namely focus distance, signal-to-noise ratio, integration time, and illumination geometry on pigment classification accuracy for a mockup using hyperspectral imaging in visible and near-infrared regions. The results obtained exemplify that the classification accuracy is influenced by the variation in these parameters. Focus distance and illumination angle have a significant effect on the classification accuracy compared to signal-to-noise ratio and integration time.

Causes of numerical pathology in formulas for reflectance factor (R), transmittance factor (T), and reflectance factor over a perfectly black background (R₀) under the Kubelka–Munk model are posited, and alternate formulas believed less prone to these pathologies are introduced. Suggestions are offered not only for R, T, and R₀, but also for intermediate or adjunct quantities used in the main formulas. Computational experiments were performed to verify that the new models produce the same results as the existing ones under non-pathological conditions, exhibit acceptable levels of precision in a customary floating-point environment, and are more robust with respect to edge cases where an input quantity is zero. The new formulas performed well, with some evidence that the new hyperbolic forms provide better accuracy than their exponential counterparts.

The surface appearance in additive manufacturing (AM) has attracted attention in recent years due to its importance in evaluating the quality of 3D printed structures. Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), holds an important share of the AM market because of its large economic potential in many industries. Nevertheless, the quality assurance procedure for FDM manufactured parts is usually complicated and expensive. The enhancement of the appearance at different illumination and viewing angles can be exploited in various applications, such as civil engineering, aeronautics, medical fields, and art. There are two steps in improving the microstructure and material appearance of printed objects, including pre-processing and post-processing. This study aims to elucidate the role of the pre-processing phase in the development of FDM parts through the assessment of color differences. For this purpose, a set of polymeric samples with different wedge (slope) angles were 3D printed using an FDM printer. The color difference between the elements is discussed and correlated with the pre-processing parameters. It is revealed that the wedge angle of the elements in the design, slicing process, and infill density could alter the color appearance of the printed parts in a predictable trend. This research suggests that low infill density and wedge angles in polylactide filaments can result in a more stable color appearance.

In this article, the authors propose a method to estimate the ink layer layout for a three-dimensional (3D) printer. This enables 3D printed skin to be produced with the desired translucency, which they represent as line spread function (LSF). A deep neural network in an encoder–decoder model is used for the estimation. It was previously reported that machine learning is an effective way to formulate the complex relationship between optical properties such as LSF and the ink layer layout in a 3D printer. However, although 3D printers are more widespread, the printing process is still time-consuming. Hence, it may be difficult to collect enough data to train a neural network sufficiently. Therefore, in this research, they prepare the training data, which is the correspondence between an LSF and the ink layer layout in a 3D printer, via computer simulation. They use a method to simulate the subsurface scattering of light for multilayered media. The deep neural network was trained with the simulated data and evaluated using a CG skin object. The result shows that their proposed method can estimate an appropriate ink layer layout that closely reproduces the target color and translucency.