scholarly journals Spectral Reconstruction Using an Iteratively Reweighted Regulated Model from Two Illumination Camera Responses

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7911
Author(s):  
Zhen Liu ◽  
Kaida Xiao ◽  
Michael R. Pointer ◽  
Qiang Liu ◽  
Changjun Li ◽  
...  
Keyword(s):  
Spectral Reflectance ◽  
Imaging System ◽  
Estimation Method ◽  
Color Difference ◽  
Graphic Arts ◽  
Spectral Reconstruction ◽  
Acceptable Range ◽  
Rgb Images ◽  
Improved Accuracy ◽  
Specular Highlights

An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry.

scholarly journals Developing a multi-spectral imaging system using a RGB camera under two illuminations

2020 ◽  
Vol 2020 (28) ◽  
pp. 277-281
Author(s):  
Zhen Liu ◽  
Kaida Xiao ◽  
Michael Pointer ◽  
Changjun Li
Keyword(s):  
Imaging System ◽  
Estimation Method ◽  
Spectral Imaging ◽  
Color Temperature ◽  
Lighting Conditions ◽  
Model Combining ◽  
Rgb Images ◽  
Spectra Properties ◽  
Different Color ◽  
Specular Highlights

This paper proposes a multi-spectral imaging system, developed using a commercial-grade camera, under two commonly used illumination. Rather than using conventional direct or diffuse light, the novelty of our method is to use a cross-polarized imaging system to eliminate glare and specular highlights. Two RGB images are captured under two different color temperature lighting conditions. An improved reflectance estimation method is developed to transform camera RGB under two illumination to spectral reflectance using a regulated model, combining the polynomial expansion of the camera signals with optimally selected feature. The method was tested using both a semi-gloss ColorChecker SG (140) and matte ColorChecker DC (240) chart. The results indicate that the proposed method significantly outperforms the traditional methods both in terms of spectra and colorimetric accuracy. This new multi-spectral imaging system is sufficiently precise to predict spectra properties and its performance within an acceptable range.

Color reproduction of human skin by spectral reflectance using RGB images and the Wiener estimation method

2017 ◽  
Author(s):  
Kiyomi Sato ◽  
Shota Miyazawa ◽  
Hideki Funamizu ◽  
Tomonori Yuasa ◽  
Izumi Nishidate ◽  
...  
Keyword(s):  
Human Skin ◽  
Spectral Reflectance ◽  
Estimation Method ◽  
Color Reproduction ◽  
Wiener Estimation ◽  
Rgb Images

Spectral-Reflectance Estimation under Multiple Light Sources

2021 ◽  
Vol 2021 (29) ◽  
pp. 25-30
Author(s):  
Shoji Tominaga
Keyword(s):  
Spectral Reflectance ◽  
Imaging System ◽  
Dielectric Material ◽  
Image Data ◽  
Light Sources ◽  
Surface Geometry ◽  
Everyday Objects ◽  
Comprehensive Method ◽  
Inhomogeneous Dielectric ◽  
Specular Highlights

We describe a comprehensive method for estimating the surface-spectral reflectance from the image data of objects acquired under multiple light sources. This study uses the objects made of an inhomogeneous dielectric material with specular highlights. A spectral camera is used as an imaging system. The overall appearance of objects in a scene results from the chromatic factors such as reflectance and illuminant and the shading terms such as surface geometry and position. We first describe the method of estimating the illuminant spectra of multiple light sources based on detecting highlights appearing on object surfaces. The highlight candidates are detected first, and then some appropriate highlight areas are interactively selected among the candidates. Next, we estimate the spectral reflectance from a wide area selected from an object's surface. The color signals observed from the selected area are described using the estimated illuminant spectra, the surfacespectral reflectance, and the shading terms. This estimation utilizes the fact that the definition domains of reflectance and shading terms are different in each other. We develop an iterative algorithm for estimating the reflectance and the shading terms in two steps repeatedly. Finally, the feasibility of the proposed method is confirmed in an experiment using everyday objects under the illumination environment with multiple light sources.

Color adjustment algorithm adapted to the spectral reflectance estimation method

2014 ◽  
Vol 21 (3) ◽  
pp. 369-372 ◽  
Author(s):  
Tomonori Yuasa ◽  
Ryosuke Honma ◽  
Hideki Funamizu ◽  
Izumi Nishidate ◽  
Yoshihisa Aizu
Keyword(s):  
Spectral Reflectance ◽  
Estimation Method

scholarly journals Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

scholarly journals Estimation of Melanin and Hemoglobin Using Spectral Reflectance Images Reconstructed from a Digital RGB Image by the Wiener Estimation Method

Sensors ◽  
2013 ◽  
Vol 13 (6) ◽  
pp. 7902-7915 ◽  
Author(s):  
Izumi Nishidate ◽  
Takaaki Maeda ◽  
Kyuichi Niizeki ◽  
Yoshihisa Aizu
Keyword(s):  
Spectral Reflectance ◽  
Estimation Method ◽  
Wiener Estimation ◽  
Rgb Image

Investigating the Upper-Bound Performance of Sparse-Coding-Based Spectral Reconstruction from RGB Images

2021 ◽  
Vol 2021 (29) ◽  
pp. 19-24
Author(s):  
Yi-Tun Lin ◽  
Graham D. Finlayson
Keyword(s):  
Sparse Coding ◽  
Upper Bound ◽  
Linear Mapping ◽  
Spectral Space ◽  
Spectral Reconstruction ◽  
Image Patches ◽  
Coding Method ◽  
Rgb Images ◽  
Spectral Neighborhood ◽  
Better Than

In Spectral Reconstruction (SR), we recover hyperspectral images from their RGB counterparts. Most of the recent approaches are based on Deep Neural Networks (DNN), where millions of parameters are trained mainly to extract and utilize the contextual features in large image patches as part of the SR process. On the other hand, the leading Sparse Coding method ‘A+’—which is among the strongest point-based baselines against the DNNs—seeks to divide the RGB space into neighborhoods, where locally a simple linear regression (comprised by roughly 102 parameters) suffices for SR. In this paper, we explore how the performance of Sparse Coding can be further advanced. We point out that in the original A+, the sparse dictionary used for neighborhood separations are optimized for the spectral data but used in the projected RGB space. In turn, we demonstrate that if the local linear mapping is trained for each spectral neighborhood instead of RGB neighborhood (and theoretically if we could recover each spectrum based on where it locates in the spectral space), the Sparse Coding algorithm can actually perform much better than the leading DNN method. In effect, our result defines one potential (and very appealing) upper-bound performance of point-based SR.

Sign in / Sign up

Export Citation Format

Share Document