Multispectral near-infrared imaging for wetness estimation.

Estimation of the wetness of objects is an important technique for recognizing states in the real world. In this paper, we propose a non-contact method for estimating the wetness of objects using multispectral near-infrared (NIR) imaging. In contrast with a previous method that requires hyperspectral (110-band) images taken with fine spectral resolution (5 nm intervals) to estimate the degree of wetness, our method enables accurate wetness estimation using few-band NIR images with coarse spectral resolution (40 nm intervals). In general, water absorbs a substantial amount of incident light at wavelengths around 1000 nm and a smaller amount at wavelengths around 900 nm. This phenomenon indicates that the light absorption coefficient of water particularly varies over the NIR spectral band. These differences in the light absorption coefficients of water in the NIR bands are exploited in the model we derived for the appearance of a wet object surface, facilitating accurate wetness estimation. The effectiveness of the proposed method is demonstrated experimentally.

Concept of dual-resolution light field imaging using an organic photoelectric conversion film for high-resolution light field photography.

Light field imaging is an emerging technique that is employed to realize various applications such as multi-viewpoint imaging, focal-point changing, and depth estimation. In this paper, we propose a concept of a dual-resolution light field imaging system to synthesize super-resolved multi-viewpoint images. The key novelty of this study is the use of an organic photoelectric conversion film (OPCF), which is a device that converts spectra information of incoming light within a certain wavelength range into an electrical signal (pixel value), for light field imaging. In our imaging system, we place the OPCF having the green spectral sensitivity onto the micro-lens array of the conventional light field camera. The OPCF allows us to acquire the green spectra information only at the center viewpoint with the full resolution of the image sensor. In contrast, the optical system of the light field camera in our imaging system captures the other spectra information (red and blue) at multiple viewpoints (sub-aperture images) but with low resolution. Thus, our dual-resolution light field imaging system enables us to simultaneously capture information about the target scene at a high spatial resolution as well as the direction information of the incoming light. By exploiting these advantages of our imaging system, our proposed method enables the synthesis of full-resolution multi-viewpoint images. We perform experiments using synthetic images, and the results demonstrate that our method outperforms other previous methods.

Non-Contact Heart Rate Estimation via Adaptive RGB/NIR Signal Fusion.

We propose a non-contact heart rate (HR) estimation method that is robust to various situations, such as bright, low-light, and varying illumination scenes. We utilize a camera that records red, green, and blue (RGB) and near-infrared (NIR) information to capture the subtle skin color changes induced by the cardiac pulse of a person. The key novelty of our method is the adaptive fusion of RGB and NIR signals for HR estimation based on the analysis of background illumination variations. RGB signals are suitable indicators for HR estimation in bright scenes. Conversely, NIR signals are more reliable than RGB signals in scenes with more complex illumination, as they can be captured independently of the changes in background illumination. By measuring the correlations between the lights reflected from the background and facial regions, we adaptively utilize RGB and NIR observations for HR estimation. The experiments demonstrate the effectiveness of the proposed method.

Enhancing Color Images of Extremely Low Light Scenes Based on RGB/NIR Images Acquisition With Different Exposure Times.

We propose a novel method to synthesize a noise- and blur-free color image sequence using near-infrared (NIR) images captured in extremely low light conditions. In extremely low light scenes, heavy noise and motion blur are simultaneously produced in the captured images. Our goal is to enhance the color image sequence of an extremely low light scene. In this paper, we augment the imaging system as well as enhancing the image synthesis scheme. We propose a novel imaging system that can simultaneously capture the red, green, blue (RGB) and the NIR images with different exposure times. An RGB image is taken with a long exposure time to acquire sufficient color information and mitigates the effects of heavy noise. By contrast, the NIR images are captured with a short exposure time to measure the structure of the scenes. Our imaging system using different exposure times allows us to ensure sufficient information to reconstruct a clear color image sequence. Using the captured image pairs, we reconstruct a latent color image sequence using an adaptive smoothness condition based on gradient and color correlations. Our experiments using both synthetic images and real image sequences show that our method outperforms other state-of-the-art methods.