Reliability-Aware Restoration Framework for 4D Spectral Photoacoustic Data.

Spectral photoacoustic imaging (PAI) is a new technology that is able to provide 3D geometric structure associated with 1D wavelength-dependent absorption information of the interior of a target in a non-invasive manner. It has potentially broad applications in clinical and medical diagnosis. Unfortunately, the usability of spectral PAI is severely affected by a time-consuming data scanning process and complex noise. Therefore in this study, we propose a reliability-aware restoration framework to recover clean 4D data from incomplete and noisy observations. To the best of our knowledge, this is the first attempt for the 4D spectral PA data restoration problem that solves data completion and denoising simultaneously. We first present a sequence of analyses, including modeling of data reliability in the depth and spectral domains, developing an adaptive correlation graph, and analyzing local patch orientation. On the basis of these analyses, we explore global sparsity and local self-similarity for restoration. We demonstrated the effectiveness of our proposed approach through experiments on real data captured from patients, where our approach outperformed the state-of-the-art methods in both objective evaluation and subjective assessment.

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.

Hybrid High Dynamic Range Imaging fusing Neuromorphic and Conventional Images.

Reconstruction of high dynamic range image from a single low dynamic range image captured by a conventional RGB camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras like event camera and spike camera can record high dynamic range scenes in the form of intensity maps, but with much lower spatial resolution and no color information. In this article, we propose a hybrid imaging system (denoted as NeurImg) that captures and fuses the visual information from a neuromorphic camera and ordinary images from an RGB camera to reconstruct high-quality high dynamic range images and videos. The proposed NeurImg-HDR+ network consists of specially designed modules, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images to reconstruct high-resolution, high dynamic range images and videos. We capture a test dataset of hybrid signals on various HDR scenes using the hybrid camera, and analyze the advantages of the proposed fusing strategy by comparing it to state-of-the-art inverse tone mapping methods and merging two low dynamic range images approaches. Quantitative and qualitative experiments on both synthetic data and real-world scenarios demonstrate the effectiveness of the proposed hybrid high dynamic range imaging system. Code and dataset can be found at: https://github.com/hjynwa/NeurImg-HDR.

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.

Depth estimation using spectrally varying defocus blur.

This paper proposes a method to estimate depth from a single multispectral image by using a lens property known as chromatic aberration. Chromatic aberration causes light passing through a lens to be refracted depending on the wavelength. The refraction causes the angle of rays to vary depending on their wavelength and a change in focal length, which leads to a defocus blur for different wavelengths. We propose a theory to recover a continuous depth map from the blur in a single multispectral image that includes chromatic aberration. The proposed method needs only a standard wide-aperture lens, which naturally exhibits chromatic aberration, and a multispectral camera. Moreover, we use a simple yet effective depth-of-field synthesis method to calculate the derivatives and obtain all-in-focus images necessary to approximate spectral derivatives. We verified the effectiveness of the proposed method on various real-world scenes.

A Microfacet-Based Model for Photometric Stereo with General Isotropic Reflectance.

This paper presents a precise, stable, and invertible reflectance model for photometric stereo. This microfacet-based model is applicable to all types of isotropic surface reflectance, covering cases from diffusion to specular reflections. We introduce a single variable to physically quantify the surface smoothness, and by monotonically sliding this variable between 0 and 1, our model enables a versatile representation that can smoothly transform between an ellipsoid of revolution and the equation for Lambertian reflectance. In the inverse domain, this model offers a compact and physically interpretable formulation, for which we introduce a fast and lightweight solver that allows accurate estimations for both surface smoothness and surface shape. Finally, extensive experiments on the appearances of synthesized and real objects evidence that this model is state-of-the-art in our off-the-shelf solution.

Depth Sensing by Near-Infrared Light Absorption in Water.

This paper introduces a novel depth recovery method based on light absorption in water. Water absorbs light at almost all wavelengths whose absorption coefficient is related to the wavelength. Based on the Beer-Lambert model, we introduce a bispectral depth recovery method that leverages the light absorption difference between two near-infrared wavelengths captured with a distant point source and orthographic cameras. Through extensive analysis, we show that accurate depth can be recovered irrespective of the surface texture and reflectance, and introduce algorithms to correct for nonidealities of a practical implementation including tilted light source and camera placement, nonideal bandpass filters and the perspective effect of the camera with a diverging point light source. We construct a coaxial bispectral depth imaging system using low-cost off-the-shelf hardware and demonstrate its use for recovering the shapes of complex and dynamic objects in water. We also present a trispectral variant to further improve robustness to extremely challenging surface reflectance. Experimental results validate the theory and practical implementation of this novel depth recovery paradigm, which we refer to as shape from water.

Reconstruction of Geometric and Optical Parameters of Non-Planar Objects with Thin Film.

Here, we propose a novel method to estimate the parameters of non-planar objects with thin film surfaces. Being able to estimate the optical parameters of objects with thin film surfaces has a wide range of applications from industrial inspections to biological and archaeology research. However, there are many challenging issues that need to be overcome to model such parameters. The appearance of thin film objects is highly dependent on the surface orientation and optical parameters such as the refractive index and film thickness. First, we therefore analyzed the optical parameters of non-planar objects with thin film surfaces. Next, we proposed and implemented an analysis procedure and demonstrated its effectiveness for studying planar objects with thin film surfaces. Finally, we developed a device to acquire the shapes and optical parameters of objects with thin film surfaces using a camera and demonstrated the effectiveness of our method experimentally. Then, we surveyed the errors caused by the light source. We discussed the difference between the theoretically obtained parameters and experimental data obtained using a hyper spectral camera.

Hyperspectral Image Super-Resolution with a Mosaic RGB Image.

Recently, many hyperspectral (HS) image superresolution methods that merge a low spatial resolution HS image and a high spatial resolution three-channel RGB image have been proposed in spectral imaging. A largely ignored fact is that most existing commercial RGB cameras capture high resolution images by a single CCD/CMOS sensor equipped with a color filter array (CFA). In this paper, we account for the common imaging mechanism of commercial RGB cameras, and propose to use a mosaic RGB image for HS image super-resolution, which prevents demosaicing error and thus its propagation into the HS image super-resolution results. We design a proper nonlocal low-rank regularization to exploit the intrinsic properties - rich self-repeating patterns and high correlation across spectra - within HS images of natural scenes, and formulate the HS image super-resolution task into a variational optimization problem, which can be efficiently solved via the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method has been evaluated on two benchmark datasets, demonstrating that the proposed method can provide substantial improvement over the current state-of-the-art HS image superresolution methods without considering the mosaicing effect. Finally, we show that our method can also perform well in the real capture system.

SymPS: BRDF Symmetry Guided Photometric Stereo for Shape and Light Source Estimation.

We propose uncalibrated photometric stereo methods that address the problem due to unknown isotropic reflectance. At the core of our methods is the notion of "constrained half-vector symmetry" for general isotropic BRDFs. We show that such symmetry can be observed in various real-world materials, and it leads to new techniques for shape and light source estimation. Based on the 1D and 2D representations of the symmetry, we propose two methods for surface normal estimation; one focuses on accurate elevation angle recovery for surface normals when the light sources only cover the visible hemisphere, and the other for comprehensive surface normal optimization in the case that the light sources are also non-uniformly distributed. The proposed robust light source estimation method also plays an essential role to let our methods work in an uncalibrated manner with good accuracy. Quantitative evaluations are conducted with both synthetic and real-world scenes, which produce the state-of-the-art accuracy for all of the non-Lambertian materials in MERL database and the real-world datasets.

Hyperspectral photometric stereo for a single capture.

We present a single-capture photometric stereo method using a hyperspectral camera. A spectrally and spatially designed illumination enables a point-wise estimation of reflectance spectra and surface normals from a single hyperspectral image. The illumination works as a reflectance probe in wide spectral regions where reflectance spectra are measured, and the full spectra are estimated by interpolation. It also works as the resource for shadings in other spectral regions. The accuracy of estimation is evaluated in a simulation. Also, we prepare an experimental setup and demonstrate a surface reconstruction against a real scene.

Reflectance and Fluorescence Spectral Recovery via Actively Lit RGB Images.

In recent years, fluorescence analysis of scenes has received attention in computer vision. Fluorescence can provide additional information about scenes, and has been used in applications such as camera spectral sensitivity estimation, 3D reconstruction, and color relighting. In particular, hyperspectral images of reflective-fluorescent scenes provide a rich amount of data. However, due to the complex nature of fluorescence, hyperspectral imaging methods rely on specialized equipment such as hyperspectral cameras and specialized illuminants. In this paper, we propose a more practical approach to hyperspectral imaging of reflective-fluorescent scenes using only a conventional RGB camera and varied colored illuminants. The key idea of our approach is to exploit a unique property of fluorescence: the chromaticity of fluorescent emissions are invariant under different illuminants. This allows us to robustly estimate spectral reflectance and fluorescent emission chromaticity. We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated. We demonstrate in results that all scene spectra can be accurately estimated from RGB images. Finally, we show that our method can be used to accurately relight scenes under novel lighting.

Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain.

Hyperspectral imaging is beneficial to many applications but most traditional methods do not consider fluorescent effects which are present in everyday items ranging from paper to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflection and fluorescence so proper separation of these components is necessary for analyzing them. In recent years, effective imaging methods have been proposed but most require capturing the scene under multiple illuminants. In this paper, we demonstrate efficient separation and recovery of reflectance and fluorescence emission spectra through the use of two high frequency illuminations in the spectral domain. With the obtained fluorescence emission spectra from our high frequency illuminants, we then describe how to estimate the fluorescence absorption spectrum of a material given its emission spectrum. In addition, we provide an in depth analysis of our method and also show that filters can be used in conjunction with standard light sources to generate the required high frequency illuminants. We also test our method under ambient light and demonstrate an application of our method to synthetic relighting of real scenes.

From Intensity Profile to Surface Normal: Photometric Stereo for Unknown Light Sources and Isotropic Reflectances.

We propose an uncalibrated photometric stereo method that works with general and unknown isotropic reflectances. Our method uses a pixel intensity profile, which is a sequence of radiance intensities recorded at a pixel under unknown varying directional illumination. We show that for general isotropic materials and uniformly distributed light directions, the geodesic distance between intensity profiles is linearly related to the angular difference of their corresponding surface normals, and that the intensity distribution of the intensity profile reveals reflectance properties. Based on these observations, we develop two methods for surface normal estimation; one for a general setting that uses only the recorded intensity profiles, the other for the case where a BRDF database is available while the exact BRDF of the target scene is still unknown. Quantitative and qualitative evaluations are conducted using both synthetic and real-world scenes, which show the state-of-the-art accuracy of smaller than 10 degree without using reference data and 5 degree with reference data for all 100 materials in MERL database.

Image-based separation of reflective and fluorescent components using illumination variant and invariant color.

Traditionally, researchers tend to exclude fluorescence from color appearance algorithms in computer vision and image processing because of its complexity. In reality, fluorescence is a very common phenomenon observed in many objects, from gems and corals, to different kinds of writing paper, and to our clothes. In this paper, we provide detailed theories of fluorescence phenomenon. In particular, we show that the color appearance of fluorescence is unaffected by illumination in which it differs from ordinary reflectance. Moreover, we show that the color appearance of objects with reflective and fluorescent components can be represented as a linear combination of the two components. A linear model allows us to separate the two components using images taken under unknown illuminants using independent component analysis (ICA). The effectiveness of the proposed method is demonstrated using digital images of various fluorescent objects.