Analysis of optical absorption of photoaged human skin using a high-frequency illumination microscopy analysis system.

Skin is composed of different layers, including the stratum corneum, epidermal living layer and papillary and reticular dermis. Each has specific optical properties due to differences in their biological components. Alterations in the skin's cutaneous biological components resulting from photoaging caused by chronic exposure to UV light affect the deterioration of appearance associated with the skin's optical properties. Various methods for analysing cutaneous optical properties have been previously proposed, including mathematical models and computer simulations. However, these were insufficient to elucidate changes in each skin layer and comprehensively understand the skin's integrated optical properties. We focused on UV-induced yellowing of the facial skin. We evaluated site-specific optical absorption of human skin tissue sections to investigate the yellowish discoloration, which is suggested to be related to the photodamage process. The method includes our original technique of separating the transmitted and scattered light using high-frequency illumination microscopy, leading to microscopic analysis of the tissue's optical absorption in the regions of interest. In analysing the sun-exposed facial skin tissue sections, we successfully showed that dermal regions of aged skin have increased absorption at 450 nm, where yellowish colours are complemented. Furthermore, we confirmed that elastic fibres with observable histological disorder resulting from photodamage are a prominent source of high optical absorption. We detected changes in the skin's optical absorption associated with dermal degeneration resulting from photodamage using a novel optical microscopy technique. The results provide a base for the evaluation of optical property changes for both yellowing discoloration and other tissue disorders.

Estimation of Wetness and Color from a Single Multispectral Image.

Recognizing wet surfaces and their degrees of wetness is essential for many computer vision applications. Surface wetness can inform us slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. The fact that surfaces darken when wet, i.e., monochromatic appearance change, has been modeled to recognize wet surfaces in the past. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about surface wetness. We first derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We present a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single multispectral image. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this is the first work to model and leverage the spectral characteristics of wet surfaces to decipher its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.