Removed images. Added instructions on how to download them from archi…

…val links instead
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diff --git a/README.md b/README.md
@@ -21,14 +21,15 @@ I welcome and encourage contributions to this project upon review. Please check
 
 ### *Raison d'être*
 
-I started out this repository by implementing, translating and collecting code snippets from the *rare* available<sup>2,3,4,5</sup> codes. Oftentimes papers are cryptical, codes are in C/C++ (requires compilation and major source code modification for general testing), or are just unavailable.
+I started out this repository by implementing, translating and collecting code snippets from the *rare* available<sup>2,3,4,5</sup> codes. Oftentimes papers are cryptical, codes are in C/C++ (requires compilation and major source code modification for general testing), or are just unavailable. See, e.g. this CSDN post<sup>6</sup> that has no valid links at all.
 
 In this context, this repository aims to be a continous algorithmic aid for ongoing research and development of SIHR methods.
 
 <sup>2</sup> Tan and Ikeuchi. [Online]. Available: <http://tanrobby.github.io/code/highlight.zip>  
 <sup>3</sup> Shen *et al.* [Online]. Available: <http://ivlab.org/publications/PR2008_code.zip>  
 <sup>4</sup> ~~Yang *et al.* [Online]. Available: <http://www6.cityu.edu.hk/stfprofile/qiyang.htm>~~  
-<sup>5</sup> Shen and Zheng. [Online]. Available: <http://ivlab.org/publications/AO2013_code.zip>
+<sup>5</sup> Shen and Zheng. [Online]. Available: <http://ivlab.org/publications/AO2013_code.zip>  
+<sup>6</sup> ~~https://blog.csdn.net/nvidiacuda/article/details/8078167~~ 
 
 ## Usage
 
@@ -95,76 +96,67 @@ Note: Akashi and Okatani's [10] method has highly fluctuating results because of
 
 ### Dataset
 
-In technical literature, there exist two ground truth datasets commonly used right now. One by Shen and Zheng [9] which is informally distributed alongside their code<sup>5</sup>, and one by Grosse *et al.* [12] in a dedicated page<sup>7</sup>. To the best of my knowledge, both are freely distributed without a license (it is of my understanding that the images implicitly exist solely for research purposes).
+In technical literature, there exist two ground truth datasets commonly used right now. One by Shen and Zheng [9] which is distributed alongside their code, and one by Grosse *et al.* [12] in a dedicated page<sup>7</sup>.
 
-As a part of the toolbox, I've included Tan and Ikeuchi's<sup>2</sup> [4], Shen *et al.*'s<sup>3</sup> [6] and Shen and Zheng's<sup>5</sup> [9] test images in [`images`](https://github.com/vitorsr/SIHR/tree/master/images).
+Other test images are included alongside the code for Shen *et al.* [6] and Yang *et al.* [8].
+
+Follow the instructions in [`images`](https://github.com/vitorsr/SIHR/tree/master/images) in order to download a local copy of these images from the respective authors' pages.
 
 <sup>7</sup> Grosse *et al.* [Online]. Available: <http://www.cs.toronto.edu/~rgrosse/intrinsic/>
 
 ### Quality
 
 Quantitative results reported are usually regarding the quality of the recovered diffuse component with respect to the ground truth available in the Shen and Zheng [9] test image set.
 
-Reproduced results below are available in the [`utils/my_quality.m`](https://github.com/vitorsr/SIHR/blob/master/utils/my_clip.m) script.
+Reproduced results below are available in the [`utils/my_quality.m`](https://github.com/vitorsr/SIHR/blob/master/utils/my_quality.m) script.
 
 #### Highest (self and peer-reported | reproduced) PSNR results (in dB)
 
-|Year| Method             | *animals* | *cups*    | *fruit*   | *masks*   | Reproduced         | *animals* | *cups* | *fruit* | *masks* |
-|:--:|--------------------|:---------:|:---------:|:---------:|:---------:|--------------------|:---------:|:------:|:-------:|:-------:|
-|2005| Tan and Ikeuchi    | 30.2      | 30.1      | 29.6      | 25.6      | Tan and Ikeuchi    | 30.4      | 31.6   | 30.4    | 25.8    |
-|2006| Yoon *et al.*      | -         | -         | -         | -         | Yoon *et al.*      | 32.9      | 33.3   | 36.6    | 34.1    |
-|2008| Shen *et al.*      | 34.6      | 37.7      | 37.6      | 31.7      | Shen *et al.*      | 34.2      | 37.5   | 38.0    | 32.1    |
-|2009| Shen and Cai       | 34.8      | 37.6      | 36.9      | 34.0      | Shen and Cai       | 34.9      | 37.6   | 36.7    | 34.0    |
-|2010| Yang *et al.*      | *37.2*    | 38.0      | 37.6      | 32.2      | Yang *et al.*      | 36.5      | 37.5   | 36.2    | 33.5    |
-|2013| Shen and Zheng     | **37.3**  | **39.3**  | *38.9*    | 34.1      | Shen and Zheng     | 37.5      | 38.3   | 38.2    | 32.7    |
-|2015| Liu *et al.*       | 33.4      | 37.6      | 35.1      | **34.5**  | -                  | -         | -      | -       | -       |
-|2016| Akashi and Okatani | 26.8      | 35.7      | 30.8      | 32.3      | Akashi and Okatani | 32.7      | 35.9   | 34.8    | 34.0    |
-|2016| Suo *et al.*       | -         | -         | **40.4**  | 34.2      | -                  | -         | -      | -       | -       |
-|2017| Ren *et al.*       | -         | 38.0      | 37.7      | **34.5**  | -                  | -         | -      | -       | -       |
-|2018| Guo *et al.*       | 35.7      | *39.1*    | 36.4      | *34.4*    | -                  | -         | -      | -       | -       |
+|Year| Method             | *animals* | *cups*    | *fruit*   | *masks*   | Reproduced   | *animals* | *cups* | *fruit* | *masks* |
+|:--:|--------------------|:---------:|:---------:|:---------:|:---------:|--------------|:---------:|:------:|:-------:|:-------:|
+|2005| Tan and Ikeuchi    | 30.2      | 30.1      | 29.6      | 25.6      | `Tan2005`    | 30.4      | 31.6   | 30.4    | 25.8    |
+|2006| Yoon *et al.*      | -         | -         | -         | -         | `Yoon2006`   | 32.9      | 33.3   | 36.6    | 34.1    |
+|2008| Shen *et al.*      | 34.6      | 37.7      | 37.6      | 31.7      | `Shen2008`   | 34.2      | 37.5   | 38.0    | 32.1    |
+|2009| Shen and Cai       | 34.8      | 37.6      | 36.9      | 34.0      | `Shen2009`   | 34.9      | 37.6   | 36.7    | 34.0    |
+|2010| Yang *et al.*      | *37.2*    | 38.0      | 37.6      | 32.2      | `Yang2010`   | 36.5      | 37.5   | 36.2    | 33.5    |
+|2013| Shen and Zheng     | **37.3**  | **39.3**  | *38.9*    | 34.1      | `Shen2013`   | 37.5      | 38.3   | 38.2    | 32.7    |
+|2015| Liu *et al.*       | 33.4      | 37.6      | 35.1      | **34.5**  | -            | -         | -      | -       | -       |
+|2016| Akashi and Okatani | 26.8      | 35.7      | 30.8      | 32.3      | `Akashi2016` | 32.7      | 35.9   | 34.8    | 34.0    |
+|2016| Suo *et al.*       | -         | -         | **40.4**  | 34.2      | -            | -         | -      | -       | -       |
+|2017| Ren *et al.*       | -         | 38.0      | 37.7      | **34.5**  | -            | -         | -      | -       | -       |
+|2018| Guo *et al.*       | 35.7      | *39.1*    | 36.4      | *34.4*    | -            | -         | -      | -       | -       |
 
 #### Highest (self and peer-reported | reproduced) SSIM results
 
-|Year| Method             | *animals* | *cups*    | *fruit*   | *masks*   | Reproduced         | *animals* | *cups* | *fruit* | *masks* |
-|:--:|--------------------|:---------:|:---------:|:---------:|:---------:|--------------------|:---------:|:------:|:-------:|:-------:|
-|2005| Tan and Ikeuchi    | 0.929     | 0.767     | 0.912     | 0.789     | Tan and Ikeuchi    | 0.928     | 0.895  | 0.907   | 0.821   |
-|2006| Yoon *et al.*      | -         | -         | -         | -         | Yoon *et al.*      | 0.980     | 0.961  | 0.961   | 0.953   |
-|2008| Shen *et al.*      | *0.974*   | 0.962     | **0.961** | *0.943*   | Shen *et al.*      | 0.975     | 0.962  | 0.961   | 0.943   |
-|2009| Shen and Cai       | -         | -         | -         | -         | Shen and Cai       | 0.985     | 0.970  | 0.962   | 0.961   |
-|2010| Yang *et al.*      | 0.970     | 0.941     | 0.939     | 0.899     | Yang *et al.*      | 0.952     | 0.937  | 0.916   | 0.896   |
-|2013| Shen and Zheng     | 0.971     | **0.966** | *0.960*   | 0.941     | Shen and Zheng     | 0.985     | 0.964  | 0.958   | 0.935   |
-|2015| Liu *et al.*       | -         | -         | -         | -         | -                  | -         | -      | -       | -       |
-|2016| Akashi and Okatani | 0.802     | 0.937     | 0.765     | 0.657     | Akashi and Okatani | 0.7340    | 0.9190 | 0.9010  | 0.8710  |
-|2016| Suo *et al.*       | -         | -         | -         | -         | -                  | -         | -      | -       | -       |
-|2017| Ren *et al.*       | 0.896     | 0.957     | 0.952     | 0.913     | -                  | -         | -      | -       | -       |
-|2018| Guo *et al.*       | **0.975** | *0.963*   | 0.930     | **0.955** | -                  | -         | -      | -       | -       |
+|Year| Method             | *animals* | *cups*    | *fruit*   | *masks*   | Reproduced   | *animals* | *cups* | *fruit* | *masks* |
+|:--:|--------------------|:---------:|:---------:|:---------:|:---------:|--------------|:---------:|:------:|:-------:|:-------:|
+|2005| Tan and Ikeuchi    | 0.929     | 0.767     | 0.912     | 0.789     | `Tan2005`    | 0.928     | 0.895  | 0.907   | 0.821   |
+|2006| Yoon *et al.*      | -         | -         | -         | -         | `Yoon2006`   | 0.980     | 0.961  | 0.961   | 0.953   |
+|2008| Shen *et al.*      | *0.974*   | 0.962     | **0.961** | *0.943*   | `Shen2008`   | 0.975     | 0.962  | 0.961   | 0.943   |
+|2009| Shen and Cai       | -         | -         | -         | -         | `Shen2009`   | 0.985     | 0.970  | 0.962   | 0.961   |
+|2010| Yang *et al.*      | 0.970     | 0.941     | 0.939     | 0.899     | `Yang2010`   | 0.952     | 0.937  | 0.916   | 0.896   |
+|2013| Shen and Zheng     | 0.971     | **0.966** | *0.960*   | 0.941     | `Shen2013`   | 0.985     | 0.964  | 0.958   | 0.935   |
+|2015| Liu *et al.*       | -         | -         | -         | -         | -            | -         | -      | -       | -       |
+|2016| Akashi and Okatani | 0.802     | 0.937     | 0.765     | 0.657     | `Akashi2016` | 0.7340    | 0.9190 | 0.9010  | 0.8710  |
+|2016| Suo *et al.*       | -         | -         | -         | -         | -            | -         | -      | -       | -       |
+|2017| Ren *et al.*       | 0.896     | 0.957     | 0.952     | 0.913     | -            | -         | -      | -       | -       |
+|2018| Guo *et al.*       | **0.975** | *0.963*   | 0.930     | **0.955** | -            | -         | -      | -       | -       |
 
 ## References
 
 <small>
 
-1. R. T. Tan, “Specularity, Specular Reflectance,” in Computer Vision, Springer US, 2014, pp. 750–752 [Online]. Available: <http://dx.doi.org/10.1007/978-0-387-31439-6_538>
-
-1. A. Artusi, F. Banterle, and D. Chetverikov, “A Survey of Specularity Removal Methods,” Computer Graphics Forum, vol. 30, no. 8, pp. 2208–2230, Aug. 2011 [Online]. Available: <http://dx.doi.org/10.1111/J.1467-8659.2011.01971.X>
-
-1. H. A. Khan, J.-B. Thomas, and J. Y. Hardeberg, “Analytical Survey of Highlight Detection in Color and Spectral Images,” in Lecture Notes in Computer Science, Springer International Publishing, 2017, pp. 197–208 [Online]. Available: <http://dx.doi.org/10.1007/978-3-319-56010-6_17>
-
-1. R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 178–193, Feb. 2005 [Online]. Available: <http://dx.doi.org/10.1109/TPAMI.2005.36>
-
-1. K. Yoon, Y. Choi, and I. S. Kweon, “Fast Separation of Reflection Components using a Specularity-Invariant Image Representation,” in 2006 International Conference on Image Processing, 2006 [Online]. Available: <http://dx.doi.org/10.1109/ICIP.2006.312650>
-
-1. H.-L. Shen, H.-G. Zhang, S.-J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recognition, vol. 41, no. 8, pp. 2461–2469, Aug. 2008 [Online]. Available: <http://dx.doi.org/10.1016/J.PATCOG.2008.01.026>
-
-1. H.-L. Shen and Q.-Y. Cai, “Simple and efficient method for specularity removal in an image,” Applied Optics, vol. 48, no. 14, p. 2711, May 2009 [Online]. Available: <http://dx.doi.org/10.1364/AO.48.002711>
-
-1. Q. Yang, S. Wang, and N. Ahuja, “Real-Time Specular Highlight Removal Using Bilateral Filtering,” in Computer Vision – ECCV 2010, Springer Berlin Heidelberg, 2010, pp. 87–100 [Online]. Available: <http://dx.doi.org/10.1007/978-3-642-15561-1_7>
-
-1. H.-L. Shen and Z.-H. Zheng, “Real-time highlight removal using intensity ratio,” Applied Optics, vol. 52, no. 19, p. 4483, Jun. 2013 [Online]. Available: <http://dx.doi.org/10.1364/AO.52.004483>
-
-1. Y. Akashi and T. Okatani, “Separation of reflection components by sparse non-negative matrix factorization,” Computer Vision and Image Understanding, vol. 146, pp. 77–85, May 2016 [Online]. Available: <http://dx.doi.org/10.1016/j.cviu.2015.09.001>
-
-1. T. Yamamoto and A. Nakazawa, “General Improvement Method of Specular Component Separation Using High-Emphasis Filter and Similarity Function,” ITE Transactions on Media Technology and Applications, vol. 7, no. 2, pp. 92–102, 2019 [Online]. Available: <http://dx.doi.org/10.3169/mta.7.92>
-
-1. R. Grosse, M. K. Johnson, E. H. Adelson, and W. T. Freeman, “Ground truth dataset and baseline evaluations for intrinsic image algorithms,” in 2009 IEEE 12th International Conference on Computer Vision, 2009 [Online]. Available: <http://dx.doi.org/10.1109/ICCV.2009.5459428>
+1. R. T. Tan, “Specularity, Specular Reflectance,” in Computer Vision, Springer US, 2014, pp. 750–752 [Online]. Available: http://dx.doi.org/10.1007/978-0-387-31439-6_538
+1. A. Artusi, F. Banterle, and D. Chetverikov, “A Survey of Specularity Removal Methods,” Computer Graphics Forum, vol. 30, no. 8, pp. 2208–2230, Aug. 2011 [Online]. Available: http://dx.doi.org/10.1111/J.1467-8659.2011.01971.X
+1. H. A. Khan, J.-B. Thomas, and J. Y. Hardeberg, “Analytical Survey of Highlight Detection in Color and Spectral Images,” in Lecture Notes in Computer Science, Springer International Publishing, 2017, pp. 197–208 [Online]. Available: http://dx.doi.org/10.1007/978-3-319-56010-6_17
+1. R. T. Tan and K. Ikeuchi, “Separating reflection components of textured surfaces using a single image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 178–193, Feb. 2005 [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2005.36
+1. K. Yoon, Y. Choi, and I. S. Kweon, “Fast Separation of Reflection Components using a Specularity-Invariant Image Representation,” in 2006 International Conference on Image Processing, 2006 [Online]. Available: http://dx.doi.org/10.1109/ICIP.2006.312650
+1. H.-L. Shen, H.-G. Zhang, S.-J. Shao, and J. H. Xin, “Chromaticity-based separation of reflection components in a single image,” Pattern Recognition, vol. 41, no. 8, pp. 2461–2469, Aug. 2008 [Online]. Available: http://dx.doi.org/10.1016/J.PATCOG.2008.01.026
+1. H.-L. Shen and Q.-Y. Cai, “Simple and efficient method for specularity removal in an image,” Applied Optics, vol. 48, no. 14, p. 2711, May 2009 [Online]. Available: http://dx.doi.org/10.1364/AO.48.002711
+1. Q. Yang, S. Wang, and N. Ahuja, “Real-Time Specular Highlight Removal Using Bilateral Filtering,” in Computer Vision – ECCV 2010, Springer Berlin Heidelberg, 2010, pp. 87–100 [Online]. Available: http://dx.doi.org/10.1007/978-3-642-15561-1_7
+1. H.-L. Shen and Z.-H. Zheng, “Real-time highlight removal using intensity ratio,” Applied Optics, vol. 52, no. 19, p. 4483, Jun. 2013 [Online]. Available: http://dx.doi.org/10.1364/AO.52.004483
+1. Y. Akashi and T. Okatani, “Separation of reflection components by sparse non-negative matrix factorization,” Computer Vision and Image Understanding, vol. 146, pp. 77–85, May 2016 [Online]. Available: http://dx.doi.org/10.1016/j.cviu.2015.09.001
+1. T. Yamamoto and A. Nakazawa, “General Improvement Method of Specular Component Separation Using High-Emphasis Filter and Similarity Function,” ITE Transactions on Media Technology and Applications, vol. 7, no. 2, pp. 92–102, 2019 [Online]. Available: http://dx.doi.org/10.3169/mta.7.92
+1. R. Grosse, M. K. Johnson, E. H. Adelson, and W. T. Freeman, “Ground truth dataset and baseline evaluations for intrinsic image algorithms,” in 2009 IEEE 12th International Conference on Computer Vision, 2009 [Online]. Available: http://dx.doi.org/10.1109/ICCV.2009.5459428
 
 </small>
diff --git a/SIHR.m b/SIHR.m
@@ -16,7 +16,7 @@
 %    SIHR % run it for a one-time session path setup
 %
 %  API:
-%    J = im2double(imread('synth.ppm')); % input image
+%    J = im2double(imread('toys.ppm')); % input image
 %    J_d = Yang2010(J); % call AuthorYEAR method
 %                       % e.g. Yang2010
 %    imshow([J, J_d, J - J_d]) % display result
@@ -58,8 +58,8 @@
 if (is_octave)
     assert(isempty(pkg('list', 'image')) == 0) % && ...
     % isempty(pkg('list', 'statistics')) == 0)
-    pkg unload image statistics
-    pkg load image statistics
+    pkg unload image % statistics
+    pkg load image % statistics
 else
     assert(isequal(license('test', 'image_toolbox'), 1)) % && ...
     % isequal(license('test', 'statistics_toolbox'), 1))