Spectral Reflectance Estimation from Camera Response Using Local Optimal Dataset and Neural Networks.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only.

High Dynamic Range Image Reconstruction from Saturated Images of Metallic Objects.

This study considers a method for reconstructing a high dynamic range (HDR) original image from a single saturated low dynamic range (LDR) image of metallic objects. A deep neural network approach was adopted for the direct mapping of an 8-bit LDR image to HDR. An HDR image database was first constructed using a large number of various metallic objects with different shapes.

Spectral Reflectance Estimation from Camera Responses Using Local Optimal Dataset.

A novel method is proposed to estimate surface-spectral reflectance from camera responses using a local optimal reflectance dataset. We adopt a multispectral imaging system that involves an RGB camera capturing multiple images under multiple light sources. A spectral reflectance database is utilized to locally determine the candidates to optimally estimate the spectral reflectance.

Improved method for spectral reflectance estimation and application to mobile phone cameras.

We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter.

Measurement and Estimation of Spectral Sensitivity Functions for Mobile Phone Cameras.

Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed.

Evaluating an image-based bidirectional reflectance distribution function measurement setup: erratum.

Two typographical errors in [Appl. Opt.57, 1918 (2018)]APOPAI0003-693510.

Spectral reconstruction of fluorescent objects with mutual illumination effects.

An approach is proposed for appearance reconstruction of fluorescent objects with mutual illumination effects under different conditions of material and illumination. We focus on the problem of reconstructing a realistic scene appearance, including mutual illumination effects, under different conditions of materials and illumination. First, spectral images of two closely located fluorescent objects are acquired under different illumination directions.

Evaluating an image-based bidirectional reflectance distribution function measurement setup.

We evaluate an image-based multiangle bidirectional reflectance distribution function measurement setup by comparing it to measurements from two commercially available goniospectrophotometers. The image-based setup uses an RGB camera to perform bidirectional measurements of the sample material. We use a conversion matrix to calculate luminance from the captured data.

Estimation of fluorescent Donaldson matrices using a spectral imaging system.

The present paper proposes a method to estimate the bispectral Donaldson matrices of fluorescent objects in a scene with a spectral imaging system. Multiple ordinary light sources with continuous spectral-power distributions are projected sequentially onto object surfaces without controlling the spectral shape of the illumination source. The estimation problem of the Donaldson matrices is solved as an optimization problem, where the residual error of observations by the spectral imaging system is minimized.

Image processing pipeline for segmentation and material classification based on multispectral high dynamic range polarimetric images.

We propose a method for the capture of high dynamic range (HDR), multispectral (MS), polarimetric (Pol) images of indoor scenes using a liquid crystal tunable filter (LCTF). We have included the adaptive exposure estimation (AEE) method to fully automatize the capturing process. We also propose a pre-processing method which can be applied for the registration of HDR images after they are already built as the result of combining different low dynamic range (LDR) images.

Spectral image analysis of mutual illumination between florescent objects.

This paper proposes a method for modeling and component estimation of the spectral images of the mutual illumination phenomenon between two fluorescent objects. First, we briefly describe the bispectral characteristics of a single fluorescent object, which are summarized as a Donaldson matrix. We suppose that two fluorescent objects with different bispectral characteristics are located close together under a uniform illumination.

Estimation of bispectral Donaldson matrices of fluorescent objects by using two illuminant projections.

This paper proposes a method for estimating the bispectral Donaldson matrices of fluorescent objects by using only two illuminant projections with continuous spectral power distributions. The Donaldson matrix represents the spectral radiance factor consisting of the sum of two components: a reflected radiance factor and a luminescent radiance factor. First, we describe the spectral characteristics of the observed matrix and model the matrix so that the luminescent radiance factor is separable into the emission and excitation wavelength components.

Imaging systems and applications: introduction to the feature.

Imaging systems have numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging system requires the integration of optics, sensing, image processing, and display rendering. This issue features original research ranging from design of stimuli for human perception, optics applications, and image enhancement to novel imaging modalities in both color and infrared spectral imaging, gigapixel imaging as well as a systems perspective to imaging.

Spectral imaging by synchronizing capture and illumination.

This paper proposes a spectral imaging technology by synchronizing a programmable light source and a high-speed monochrome camera. The light source is capable of emitting arbitrary spectrum in high speed, so that the system has the advantage of capturing spectral images without using filters. The camera and the light source are controlled by a computer in order to capture image sequence synchronously with camera and illuminant control signals.