Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction
碩士 === 國立臺灣師範大學 === 圖文傳播學系 === 103 === In this paper, we proposed an accurate recovery method of object spectral reflectance using the traditional natural neighbor interpolation, shortly named as NNI, with band-divided linear correction. Essentially, such a recovery problem was usually to transform...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2015
|
Online Access: | http://ndltd.ncl.edu.tw/handle/72139931203673405927 |
id |
ndltd-TW-103NTNU5727009 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-103NTNU57270092016-12-07T04:17:21Z http://ndltd.ncl.edu.tw/handle/72139931203673405927 Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction 以自然鄰點內插法與頻帶分段線性修正重建物體頻譜反射率之研究 Sun, Tsung-Chieh 孫琮傑 碩士 國立臺灣師範大學 圖文傳播學系 103 In this paper, we proposed an accurate recovery method of object spectral reflectance using the traditional natural neighbor interpolation, shortly named as NNI, with band-divided linear correction. Essentially, such a recovery problem was usually to transform the RGB channel values into a spectrum to simulate the reflectance of an object. There were many previous researches offering various solutions to this problem with more or less advantages and drawbacks. Our work improved the recovery result based on the interpolation approach with further correction of spectral reflectance. This new solution proposed not only gives more accurate results, but also avoids the extrapolation problem causing by the phenomena out of gamut. Our method consists of two stages of recovery procedures. First, the NNI interpolation was used to construct the spectral reflectance from the real samples of color checkers. Eight additional pre-determined spectra were imposed for the corners of the sRGB color space, named virtual extreme spectra, to guarantee all the test samples in the gamut spanned by the known samples; such that, the interpolating scheme worked well without the extrapolation problem. Secondly, the spectra resulting from NNI were further fine-tuned according to the difference between its sRGB color under illuminant of D65 and the original input one of ground true. Three pre-specified wave lengths, denoted S, M, and L, were selected as the control points to correct this NNI spectrum approaching to a new one with less color difference. This correction was composed of 4 piecewise linear transformations related to 4 bands from 400nm to S, from S to M, from M to L, and from L to 700nm respectively. Some experiments were performed to evaluate the performance of the new NNI with the virtual extreme spectra and the additional correction stage. At first, the 1269 checker spectra from Munsell book was used as the test samples under the training samples from Macbeth 24 color checkers. The largest color difference of 〖∆E〗_2000 was 1.6366 based on the illuminant of D65, and the average difference was 0.0915. And, the color differences were further improved, if the band-divided correction was adopted. Then, the largest 〖∆E〗_2000 was 1.4869, and the average difference was 0.0726. In addition, the entire gamut of sRGB was also evaluated. The spectra recovered from the specified RGB channel values lead to the largest color difference was 1.6671 and the average one was 0.0315 under the illuminant of D65, based on the training samples of Macbeth color checkers. The largest difference was 1.4915, and the average one was 0.0126, based on the training samples of Munsell book checkers. These experimental results showed that the proposed method was very accurate for the recovery of spectral reflectance. Chou, Tzren-Ru 周遵儒 2015 學位論文 ; thesis 84 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立臺灣師範大學 === 圖文傳播學系 === 103 === In this paper, we proposed an accurate recovery method of object spectral reflectance using the traditional natural neighbor interpolation, shortly named as NNI, with band-divided linear correction. Essentially, such a recovery problem was usually to transform the RGB channel values into a spectrum to simulate the reflectance of an object. There were many previous researches offering various solutions to this problem with more or less advantages and drawbacks. Our work improved the recovery result based on the interpolation approach with further correction of spectral reflectance. This new solution proposed not only gives more accurate results, but also avoids the extrapolation problem causing by the phenomena out of gamut.
Our method consists of two stages of recovery procedures. First, the NNI interpolation was used to construct the spectral reflectance from the real samples of color checkers. Eight additional pre-determined spectra were imposed for the corners of the sRGB color space, named virtual extreme spectra, to guarantee all the test samples in the gamut spanned by the known samples; such that, the interpolating scheme worked well without the extrapolation problem. Secondly, the spectra resulting from NNI were further fine-tuned according to the difference between its sRGB color under illuminant of D65 and the original input one of ground true. Three pre-specified wave lengths, denoted S, M, and L, were selected as the control points to correct this NNI spectrum approaching to a new one with less color difference. This correction was composed of 4 piecewise linear transformations related to 4 bands from 400nm to S, from S to M, from M to L, and from L to 700nm respectively.
Some experiments were performed to evaluate the performance of the new NNI with the virtual extreme spectra and the additional correction stage. At first, the 1269 checker spectra from Munsell book was used as the test samples under the training samples from Macbeth 24 color checkers. The largest color difference of 〖∆E〗_2000 was 1.6366 based on the illuminant of D65, and the average difference was 0.0915. And, the color differences were further improved, if the band-divided correction was adopted. Then, the largest 〖∆E〗_2000 was 1.4869, and the average difference was 0.0726. In addition, the entire gamut of sRGB was also evaluated. The spectra recovered from the specified RGB channel values lead to the largest color difference was 1.6671 and the average one was 0.0315 under the illuminant of D65, based on the training samples of Macbeth color checkers. The largest difference was 1.4915, and the average one was 0.0126, based on the training samples of Munsell book checkers. These experimental results showed that the proposed method was very accurate for the recovery of spectral reflectance.
|
author2 |
Chou, Tzren-Ru |
author_facet |
Chou, Tzren-Ru Sun, Tsung-Chieh 孫琮傑 |
author |
Sun, Tsung-Chieh 孫琮傑 |
spellingShingle |
Sun, Tsung-Chieh 孫琮傑 Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
author_sort |
Sun, Tsung-Chieh |
title |
Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
title_short |
Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
title_full |
Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
title_fullStr |
Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
title_full_unstemmed |
Spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
title_sort |
spectral reflectance recovery using natural neighbor interpolation with band-divided linear correction |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/72139931203673405927 |
work_keys_str_mv |
AT suntsungchieh spectralreflectancerecoveryusingnaturalneighborinterpolationwithbanddividedlinearcorrection AT sūncóngjié spectralreflectancerecoveryusingnaturalneighborinterpolationwithbanddividedlinearcorrection AT suntsungchieh yǐzìránlíndiǎnnèichāfǎyǔpíndàifēnduànxiànxìngxiūzhèngzhòngjiànwùtǐpínpǔfǎnshèlǜzhīyánjiū AT sūncóngjié yǐzìránlíndiǎnnèichāfǎyǔpíndàifēnduànxiànxìngxiūzhèngzhòngjiànwùtǐpínpǔfǎnshèlǜzhīyánjiū |
_version_ |
1718399386403209216 |