SOME ASPECTS OF PROCESSING OF MULTICHANNEL REMOTE SENSING DATA USING NEURAL NETWORKS
DOI:
https://doi.org/10.30890/2709-2313.2023-19-02-001Keywords:
Multichannel images, remote sensing, neural networksAbstract
This chapter contains the results obtained during execution of Ukrainian-French Project within “Dnipro” framework in 2022 intended on design of methods and means for processing multichannel remote sensing data using the trained neural networks. Neural netMetrics
References
Foreword to the Special Issue on Hyperspectral Image and Signal Processing /A. Plaza, J.M. Bioucas-Dias, A. Simic, W.J. Blackwell // IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing. − 2012. − Vol. 5, No 2, P. 347-353.
Chein-I Chang Hyperspectral Data Exploitation: Theory and Applications /Chein-I Chang // Wiley-Interscience. − 2007. − 440 p.
Future Trends in Remote Sensing /S. Khorram, C.F. van der Wiele, F.H. Koch, S.A.C. Nelson, M.D. Potts// In Principles of Applied Remote Sensing; Springer International Publishing, Cham, 2016. P. 277–285. ISBN 978-3-319-22559-3.
Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data /N. Kussul, G. Lemoine, F.J. Gallego, S.V. Skakun, M. Lavreniuk, A. Shelestov// IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2016. Vol. 9. P. 2500–2508. doi:10.1109/JSTARS.2016.2560141.
Van Zyl Marais, I. Onboard image quality assessment for a small low earth orbit satellite /I. van Zyl Marais, W.H. Steyn, J.A. du Preez// In Proceedings of the 7th IAA Symposium on Small Satellites for Earth Observation. Berlin, Germany. 2009. Paper IAA B7-0602 p.
Smaller Satellites, Larger Constellations: Trends and Design Issues for Earth Observation Systems /S. Marcuccio, S. Ullo, M. Carminati, O. Kanoun// IEEE Aerosp. Electron. Syst. Mag. 2019. Vol. 34. P. 50–59. doi:10.1109/MAES. 2019.2928612.
Li, Y. Image deblurring for satellite imagery using small-support-regularized deconvolution /Y. Li, K.C. Clarke// ISPRS J. Photogramm. Remote Sens. 2013. Vol. 85. P. 148–155. doi:10.1016/j.isprsjprs.2013.08.002.
Xia, H. Remote Sensing Image Deblurring Algorithm Based on WGAN /Xia H., C. Liu// In Service-Oriented Computing – ICSOC 2018 Workshops; Lecture Notes in Computer Science; Springer International Publishing. Cham, 2019. Vol. 11434. P. 113–125 ISBN 978-3-030-17641-9.
Christophe, E. Hyperspectral Data Compression Tradeoff /E. Christophe. in Optical Remote Sensing. Berlin: Springer, 2011. Vol. 8. P. 9-29.
Blanes, I. A Tutorial on Image Compression for Optical Space Imaging Systems /I. Blanes, E. Magli, J. Serra-Sagrista //IEEE Geoscience and Remote Sensing Magazine. 2014. Vol. 2, No. 3. P. 8-26.
Uss, M. Efficient Discrimination and Localization of Multimodal Remote Sensing Images Using CNN-Based Prediction of Localization Uncertainty /M. Uss, B. Vozel, V. Lukin, K. Chehdi// Remote Sensing. 2020. Vol. 12. 703 p.
Uss, M. Blind noise parameters estimation for multichannel images using deep convolutional neural networks /M. Uss, B. Vozel, V. Lukin, K. Chehdi// Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV. France. October 2019. Vol. 1115514. 11 p.
Blind Prediction of Original Image Quality for Sentinel SAR Data /O. Rubel, A. Rubel, V. Lukin, M. Carli, K. Egiazarian// In Proceedings of the 2019 8th European Workshop on Visual Information Processing (EUVIP); IEEE. Rome, Italy, 2019. P. 105–110.
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment /S. Bosse, D. Maniry, K.-R. Muller, T. Wiegand, W. Samek// IEEE Trans. Image Process. 2018. Vol. 27. P. 206–219. doi:10.1109/TIP.2017.2760518.
NoiseNet: Signal-Dependent Noise Variance Estimation with Convolutional Neural Network /M.L. Uss, B. Vozel, V. Lukin, K. Chehdi// Proceedings of the 19th International Conference, ACIVS 2018. Poitiers, France. 2018. September 24–27. 12 p.
NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency /O. Rubel, V. Lukin, A. Rubel, K. Egiazarian// Geosciences. 2019. Vol. 9, No. 7. P. 290.
Prediction of Lee filter performance for Sentinel-1 SAR images / O. Rubel, V. Lukin, A. Rubel, K. Egiazarian// IS&T International Symposium on Electronic Imaging 2020, Image Quality and System Performance. USA. January 2020. P. 371-1 – 371-6.
Li, X. Neural Image Compression and Explanation /X. Li, S. Ji// IEEE Access. 2020. 1908.08988.pdf (arxiv.org)
Neural Image Compression for Gigapixel Histopathology Image Analysis /D. Tellez et al.// IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021. Vol. 43, Iss. 2. P 567–578. https://doi.org/10.1109/TPAMI. 2019.2936841.
Full-Reference Quality Metric Based on Neural Network to Assess the Visual Quality of Remote Sensing Images /O. Ieremeiev, V. Lukin, K. Okarma, K. Egiazarian// Remote Sensing. July 2020. 31 p. DOI: 10.3390/rs12152349.
A deep neural network for image quality assessment /S. Bosse, D. Maniry, T. Wiegand, W. Samek// In Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP). IEEE: Phoenix, AZ, USA. 2016. P. 3773–3777.
A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm /T. Yuan, X. Zheng, X. Hu, W. Zhou, W. Wang // PLoS ONE. 2014. Vol. 9, No. 1. e86528. https://doi.org/10.1371/journal. pone.0086528.
Analysis of classification accuracy for pre-filtered multichannel remote sensing data /V. Lukin, S. Abramov, S. Krivenko, A. Kurekin, O. Pogrebnyak// Expert Syst. Appl. 2013. Vol. 40. P. 6400–6411.
https://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=2d (The KITTI Vision Benchmark Suite).
Image database TID2013: Peculiarities, results and perspectives /N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, C.-C. Jay Kuo// Signal Processing: Image Communication. 2015. Vol. 30. P. 57-77.
http://database.mmsp-kn.de/kadid-10k-database.html (KADID-10kIQA Data-base).
On properties of visual quality metrics in remote sensing applications /O. Ieremeiev, V. Lukin, K. Okarma, K. Egiazarian, B. Vozel// in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems. 2022. San Francisco, USA. P. 354-1354-6.
Improvement of spatial localization accuracy in learning-based patch matching using anisotropic fractal brownian motion data /M. Uss, B. Vozel, V. Lukin, K. Chehdi// Proceedings of IGARSS. July 2022. Malaysia. P. 2215-2218.
BPG-Based Automatic Lossy Compression of Noisy Images with the Prediction of an Optimal Operation Existence and Its Parameters /B. Kovalenko, V. Lukin, S. Kryvenko, V. Naumenko, B. Vozel// Appl. Sci. 2022. Vol. 12. P. 7555. https://doi.org/10.3390/ app12157555.
Lukin, V. Results of Approbation of the Method for Predicting the Classification Accuracy of Multichannel Images /V. Lukin, I. Vasilyeva, B. Vozel/ Proceedings of the 2022 IEEE 3rd International Conference on System Analysis & Intelligent Computing (SAIC). Ukraine. 2022. DOI: 10.1109/SAIC57818. 2022.9923028
Prediction of Parameters in Optimal Operation Point for BPG-based Lossy Compression of Noisy Images /B. V. Kovalenko, V. V. Lukin, S. S. Kryvenko, V. V. Naumenko, B. Vozel// Ukrainian journal of Remote Sensing. 2022. Vol. 9, No. 2. P. 4-12.
MuLoG, or HowtoApply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction? /C.-A. Deledalle, L. Denis, S. Tabti, F. Tupin// Transactions on Image Processing. 2017. Vol. 26, No. 9. P. 4389–4403.
Dellepiane, S.G. Quality Assessment of Despeckled SAR Images /S.G. Dellepiane, E. Angiati// International Geoscience and Remote Sensing Symposium. 2011. Vancouver. P. 3803-3806.
Xu, T. Remote sensing image interpolation via the nonsubsampled contourlet transform /T. Xu, Y. Fang// International ConferenceonImage Analysis and Signal Processing. 2010. Zhejiang, China. P. 695–698.
Sivakumar, R. Image Interpretation of Remote Sensing data /R. Sivakumar. Geospatial World. 2010. https://www.geospatialworld.net/article/image-interpreta-tion-of-remote-sensing-data/
“Visual Image Interpretation,” https://fis.uni bonn.de/en/recherchetools/ infobox/professionals/image- analysis/visual-image-interpretation.
Perceptual Quality Assessment of Pan-Sharpened Images /O.A. Agudelo-Medina, H.D. Benitez-Restrepo, G. Vivone, A. Bovik// Remote Sensing. 2019. Vol. 11, No. 7. P. 1-19.
Moreno-Villamarin, D.E. Predicting the Quality of Fused Long Wave Infrared and Visible Light Images /D.E. Moreno-Villamarin, H.D. Benitez-Restrepo, A.C Bovik// Transactions on Image Processing 2017. Vol. 26, No.7. P. 3479–3491.
Image matching usingstructural similarity and geometric constraint approaches on remotesensing images /J. Guo, F. Yang, H. Tan, J. Wang, Z. Liu// Journal of Applied Remote Sensing. 2016. Vol. 10, No. 4. P. 1-12.
Liu, D. No-reference remote sensing image quality assessment based on the region of interest and structural similarity /D. Liu, Y. Li, S. Chen// Proceedings of the 2nd International Conference on Advances in Image Processing. 2018. Chengdu, China. P. 64–67.
Improved robust linearized full-reference combined metric for remote sensing imaging /O. Ieremeiev, V. Abramova, K. Okarma, K. Egiazarian// Proceedings of the 6th Microwaves, Radar and Remote Sensing Symposium. 2020. Kharkiv, Ukraine. P. 443-448.
Combined No-Reference Image Quality Metrics for Visual Quality Assessment Optimized for Remote Sensing Images /A. Rubel, O. Ieremeiev, V. Lukin, J. Fastowicz, K. Okarma/ Applied Sciences. 2022. Vol.12, No.4. P. 1-19.
Unsupervised Feature Learning Framework for No-Reference Image Quality Assessment /P. Ye, J. Kumar, L. Kang, D. Doermann// Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA. 16-21 June 2012. P. 1098-1105.
Zhang, L. A Feature-Enriched Completely Blind Image Quality Evaluator /L. Zhang, L. Zhang, A.C. Bovik// IEEE Trans. Image Process. 2015 Vol. 24. P. 2579–2591.
Gonzalez, R. C. Digital Image Processing /R. C. Gonzalez, R. E.Wood// Pearson. 2018.
Burger, W. Principles of Digital Image Processing /W. Burger, M.J. Burge// Springer. 2009.
Hastie, T. The Elements of Statistical Learning /T.Hastie, R.Tibshirani, J. Friedman// Springer. 2009.
Goodfellow, I. Deep Learning /I. Goodfellow, Y. Bengio, A. Courville// MIT Press. 2016.
An Effective Image Denoising Method for UAV Images via Improved Generative Adversarial Networks /R. Wang, X. Xiao, B. Guo, Q. Qin, and R. Chen// Sensors. 2018. Vol. 18(7), No. 1985. P. 1-23
Sieberth, T. UAV image blur - its influence and ways to correct it /T. Sieberth, R. Wackrow, J. H. Chandler// The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2015. Vol. XL-1/W4.
P. 33–39.
Enhancing Contrast of Images to Improve Geometric Accuracy of a UAV Photogrammetry Project /S. Motayyeb, S.A. Fakhri, M. Varshosaz, S. Pirasteh// The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2022. Vol. XLIII-B1-2022. P.389-398.
Alfio, V.S. Influence of Image TIFF Format and JPEG Compression Level in the Accuracy of the 3D Model and Quality of the Orthophoto in UAV Photogrammetry /V. S. Alfio, D. Costantino, M. Pepe// Journal of Imaging. 2020. Vol. 6(5), No.30. P. 1-22.
UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions /Q. Hu, C. Wu, Y. Wu, N. Xiong// IEEE Access. 2019. Vol. 7. P. 91980-91991.
Kedzierski, M. Radiometric quality assessment of images acquired by UAV’s in various lighting and weather conditions /M. Kedzierski, D. Wierzbicki// Measurement. 2015. Vol. 76. P. 156-169.
A Framework for Moving Target Detection, Recognition and Tracking in UAV Videos /J. Wang, Y. Zhang, J. Lu, W. Xu// Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing. Springer, 2012.
Vol. 137.
Koretsky, G. M. A Tutorial on Electro-Optical/Infrared (EO/IR) Theory and Systems /G. M. Koretsky, J. F. Nicoll, M. S. Taylor// IDA Document D-4642. 2013.
Blind Image Quality Assessment Based on High Order Statistics Aggregation /J. Xu, P. Ye, Q. Li, H. Du, Y. Liu, D. Doermann// IEEE Trans. Image Process. 2016. Vol. 25. P. 4444–4457.
C-DIIVINE: No-Reference Image Quality Assessment Based on Local Magnitude and Phase Statistics of Natural Scenes /Y. Zhang, A.K. Moorthy, D.M. Chandler, A.C. Bovik// Signal Process. Image Commun. 2014. Vol. 29. P. 725–747.
Saad, M.A. Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain /M.A. Saad, A.C. Bovik, C. Charrier// IEEE Trans. Image Process. 2012. Vol. 21. P. 3339–3352.
Mittal, A. No-Reference Image Quality Assessment in the Spatial Domain /A. Mittal, A.K. Moorthy, A.C. Bovik// IEEE Trans. Image Process. 2012. Vol. 21. P. 4695–4708.
Moorthy, A.K. A Two-Step Framework for Constructing Blind Image Quality Indices /A.K. Moorthy, A.C. Bovik// IEEE Signal Process. Lett. 2010. Vol. 17. P. 513–516.
No-Reference Image Quality Assessment Based on Spatial and Spectral Entropies /L. Liu, B. Liu, H. Huang, A.C. Bovik// Signal Process. Image Commun. 2014. Vol. 29. P. 856–863.
Mittal, A. Making a “Completely Blind” Image Quality Analyzer /A. Mittal, R. Soundararajan, A.C. Bovik// IEEE Signal Process. Lett. 2013. Vol. 20. P. 209–212.
Xue, W. Learning without Human Scores for Blind Image Quality Assessment /W. Xue, L. Zhang, X. Mou// Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA. 2013. P. 995–1002.
Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images / K. Gu, G. Zhai, X. Yang, W. Zhang// IEEE Trans. Broadcast.. 2014. Vol. 60. P. 555–567.
Wu, Q. A Highly Efficient Method for Blind Image Quality Assessment /Q. Wu, Z. Wang, H. Li// Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP). Quebec City, Canada. 2015. P. 339–343.
Hassen, R. Image Sharpness Assessment Based on Local Phase Coherence /R. Hassen, Z. Wang, M.M.A. Salama// IEEE Trans. Image Process. 2013. Vol. 22. P. 2798–2810.
Moorthy, A.K. Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality /A.K. Moorthy, A.C. Bovik// IEEE Trans. Image Process. 2011. Vol. 20. P. 3350–3364.
No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments /L. Li, W. Lin, X. Wang, G. Yang, K. Bahrami, A.C. Kot// IEEE Trans. Cybern. 2016. Vol. 46. P. 39–50.
Blind Image Quality Assessment by Relative Gradient Statistics and Adaboosting Neural Network /L. Liu, Y. Hua, Q. Zhao, H. Huang, A.C. Bovik// Signal Process. Image Commun. 2016. Vol. 40. P. 1–15.
Blind Quality Assessment Based on Pseudo-Reference Image /X. Min, K. Gu, G. Zhai, J. Liu, X. Yang, C.W. Chen// IEEE Trans. Multimed. 2018. Vol. 20. P. 2049–2062.
Blind Image Quality Assessment Based on Multichannel Feature Fusion and Label Transfer /Q. Wu, H. Li, F. Meng, K.N. Ngan, B. Luo, C. Huang, B. Zeng// IEEE Trans. Circuits Syst. Video Technol. 2016. Vol. 26. P. 425–440.
No Reference Image Quality Assessment Metric via Multi-Domain Structural Information and Piecewise Regression /Q. Wu, H. Li, F. Meng, K.N. Ngan, S. Zhu// J. Vis. Commun. Image Represent. 2015. Vol. 32. P. 205–216.
No-Reference Quality Assessment of Deblurred Images Based on Natural Scene Statistics /L. Li, Y. Yan, Z. Lu, J. Wu, K. Gu, S. Wang// IEEE Access. 2017. Vol. 5. P. 2163–2171.
Rakhshanfar, M. Sparsity-Based No-Reference Image Quality Assessment for Automatic Denoising /M. Rakhshanfar, M.A. Amer// Signal Image Video Process. 2018. Vol. 12. P. 739–747.
DipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs /K. Ma, W. Liu, T. Liu, Z. Wang, D. Tao// IEEE Trans. Image Process. 2017. Vol. 26. P. 3951–3964.
Bahrami, K. A Fast Approach for No-Reference Image Sharpness Assessment Based on Maximum Local Variation /K. Bahrami, A.C. Kot// IEEE Signal Process. Lett. 2014. Vol. 21. P. 751–755.
Vu, P.V. A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation /P.V. Vu, D.M. Chandler// IEEE Signal Process. Lett. 2012. Vol. 19. P. 423–426.
Ferzli, R. A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB) /R. Ferzli, L.J. Karam// IEEE Trans. Image Process. 2009. Vol. 18. P. 717–728.
Zhang, Y. No-Reference Image Quality Assessment Based on Log-Derivative Statistics of Natural Scenes /Y. Zhang, D.M. Chandler// J. Electron. Imaging. 2013. Vol. 22. 043025 p.
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features /W. Xue, X. Mou, L. Zhang, A.C. Bovik, X. Feng// IEEE Trans. Image Process. 2014. Vol. 23. P. 4850–4862.
No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization /K. Gu, W. Lin, G. Zhai, X. Yang, W. Zhang, C.W. Chen// IEEE Trans. Cybern. 2017. Vol. 47. P. 4559–4565.
No-Reference Image Sharpness Assessment in Autoregressive Parameter Space /K. Gu, G. Zhai, W. Lin, X. Yang, W. Zhang// IEEE Trans. Image Process. 2015. Vol. 24. P. 3218–3231.
Narvekar, N.D. A No-Reference Perceptual Image Sharpness Metric Based on a Cumulative Probability of Blur Detection /N.D. Narvekar, L.J. Karam// Proceedings of the 2009 International Workshop on Quality of Multimedia Experience (QoMEx). San Diego, USA. 2009. P. 87–91.
Feichtenhofer, C. A Perceptual Image Sharpness Metric Based on Local Edge Gradient Analysis /C. Feichtenhofer, H. Fassold, P. Schallauer// IEEE Signal Process. Lett. 2013. Vol. 20. P. 379–382.
Sharpness Metric for No-Reference Image Visual Quality Assessment /N.N. Ponomarenko, V.V. Lukin, O.I. Eremeev, K.O. Egiazarian, J.T. Astola// International Society for Optical Engineering. Bellingham, USA. 2012. 829519 p.
Zhu, T. A No-Reference Objective Image Quality Metric Based on Perceptually Weighted Local Noise /T. Zhu, L. Karam// EURASIP J. Image Video Process., 2014. 2014. Vol.5.
Gong, Y. Image Enhancement by Gradient Distribution Specification / Y. Gong, I.F. Sbalzarini// In Lecture Notes in Computer Science. Cham, Switzerland. 2015. Vol. 9009. P. 47–62.
The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric /F. Crété-Roffet, T. Dolmiere, P. Ladret, M. Nicolas// In Human Vision and Electronic Imaging XII; International Society for Optics and Photonics. San Jose, USA. 2007. 649201 p.
Golestaneh, S.A. No-Reference Quality Assessment of JPEG Images via a Quality Relevance Map /S.A. Golestaneh, D.M. Chandler// IEEE Signal Process. Lett. 2014. Vol. 21. P. 155–158.
Still image/video frame lossy compression providing a desired visual quality
/A. Zemliachenko, V. Lukin, N. Ponomarenko, K. Egiazarian, J. Astola// Multidimensional Systems Signal Processing: Image Communication. 2016. Vol. 27, No. 3. P. 697-718.
Li, F. A Two-step Procedure for Image Lossy Compression by ADCTC With a Desired Quality /F. Li, S. Krivenko, V. Lukin// 2020 IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT). 2020. P. 307-312. doi: 10.1109/DESSERT50317.2020.9125000.
Lossy compression of three-channel remote sensing images with controllable quality /I. Vasilyeva, F. Li, S. Abramov, V. V. Lukin, B. Vozel, K. Chehdi// Image and Signal Processing for Remote Sensing XXVII. 2021. Vol. 11862. P. 199-210.
Quality control for the BPG lossy compression of three-channel remote sensing images /F. Li, V. Lukin, O. Ieremeiev, K. Okarma// Remote Sensing. 2022. Vol. 14, No. 8. 1824 p.
Smart lossy compression of images based on distortion prediction /S. Krivenko, O. Krylova, E. Bataeva, V. Lukin// Telecommunications Radio Engineering. 2018. Vol. 77, No. 17.
Prediction of visual quality metrics in lossy image compression /S. Krivenko, F. Li, V. Lukin, B. Vozel, O. Krylova// 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO). 2020. P. 478-483. doi: 10.1109/ ELNANO50318.2020.9088819.
Кривенко, С.С. Прогнозирование параметров вносимых искажений при сжатии изображений с потерями /С.С. Кривенко, М.С. Зряхов, В.В. Лукин// Радиоэлектроника и информатика, ХНУРЭ. Харьков. 2018. № 2. – C. 22-29.
Is Texture Denoising Efficiency Predictable /O. Rubel, S. Abramov, V.Lukin, K. Egiazarian, B. Vozel, A. Pogrebnyak// International Journal on Pattern Recognition and Artificial Intelligence. 2018. Vol. 32, No. 1860005. 32 p.
Rubel, A. On Prediction of Image Denoising Expedience Using Neural Networks /A. Rubel, O. Rubel, V. Lukin/ Proceedings of PICST. October 2018. Kharkov, Ukraine. P. 629-634.
DCT Based High Quality Image Compression /N.N. Ponomarenko, V.V. Lukin, K.Egiazarian, J. Astola// Proceedings of 14th Scandinavian Conference on Image Analysis. Joensuu, Finland. June, 2005. P. 1177-1185.
ADCT: A new high quality DCT based coder for lossy image compression /N. Ponomarenko, V. Lukin, K. Egiazarian, J. Astola// CD ROM Proceedings of LNLA. Switzerland. August 2008. 6 p.
On Image Complexity in Viewpoint of Image Processing Performance /V. Lukin, S. Krivenko, F. Li, S. Abramov, V. Makarichev// Proceedings of the conference “Intelligent Information technologies and systems of information security” IntelITSIS-2022. Ukraine. 2022. 16 p.
Intelligent lossy compression method of providing a desired visual quality for images of different complexity /F. Li, V.V. Lukin, K. Okarma, Y. Fu, J. Duan// Proceedings of AMMCS. China. 2021. P. 500-505.
Zabala, A. Impact of lossy compression on mapping crop areas from remote sensing /A. Zabala, X. Pons// International Journal of Remote Sensing. 2013. Vol. 34, No. 8. P. 2796-2813.
Effects of JPEG and JPEG2000 Lossy Compression on Remote Sensing Image Classification for Mapping Crops and Forest Areas /A. Zabala, X. Pons, R. Diaz-Delgado, F. Garcia, F. Auli-Llinas, J. Serra-Sagrista// 2006 IEEE International Symposium on Geoscience and Remote Sensing. 2006. P.790-793. DOI: 10.1109/IGARSS.2006.203.
Krivenko, S. Visually Lossless Compression of Dental Images /S. Krivenko, V. Lukin, O. Krylova// 2019 IEEE 39th International Conference on Electronics and Nanotechnology (ELNANO). 2019. P. 394-399.
Efficient Prediction of the First Just Noticeable Difference Point for JPEG Compressed Images /B. Bondžulić, N. Stojanović, V. Petrović, B. Pavlović,
Z. Miličević// Acta Polytechnica Hungarica. 2021. Vol. 18, No.8. P.201-220. DOI:10.12700/APH.18.8.2021.8.11.
Taubman, D.S. JPEG2000: image compression fundamentals, standards, and practice / D.S. Taubman, M.W. Marcellin. 2013.
Said, A. A new, fast, and efficient image codec based on set partitioning in hierarchical trees /A. Said, W. A. Pearlman// IEEE Transactions on Circuits and Systems for Video Technology. 1996. Vol. 6, No.3. P. 243-250. DOI: 10.1109/ 76.499834.
Medical image compression based on region of interest using better portable graphics (BPG) /D. Yee, S. Soltaninejad, D. Hazarika, G. Mbuyi, R. Barnwal, A. Basu// 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2017. P. 216-221. DOI: 10.1109/SMC.2017.8122605.
Albalawi, U. Energy-Efficient Design of the Secure Better Portable Graphics Compression Architecture for Trusted Image Communication in the IoT /U. Albalawi, S. P. Mohanty, E. Kougianos// 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). 2016. P. 302-307. DOI: 10.1109/ISVLSI.2016.21.
Analysis of noisy image lossy compression by BPG using visual quality metrics /B. Kovalenko, V. Lukin, V. Naumenko, S. Krivenko// 2021 IEEE 3rd International Conference on Advanced Trends in Information Theory (ATIT). 2021. P. 20-25. DOI: 10.1109/ATIT54053.2021.9678575.
Zhai, G. Perceptual image quality assessment: a survey /G. Zhai, X. Min// Science China Information Sciences. 2020. Vol. 63. P. 1-52.
Zhai, G. Perceptual image quality assessment: a survey /G. Zhai, X. Min// Science China Information Sciences. 2020. Vol. 63. P. 1-52.
Chang, S. G. Image denoising via lossy compression and wavelet thresholding /S. G. Chang, B. Yu, M. Vetterli// Proceedings of International Conference on Image Processing. 1997. Vol. 1. P. 604-607. DOI: 10.1109/ICIP.1997.647985.
Lossy Compression of Noisy Remote Sensing Images with Prediction of Optimal Operation Point Existence and Parameters / A. Zemliachenko, S. Abramov, V. Lukin, B. Vozel, K. Chehdi// SPIE Journal on Advances in Remote Sensing. 2015. Vol. 9, No. 1. p. 26. DOI: 10.1117/1.JRS.9.095066.
Naumenko, V. Lossy compression of single-channel images corrupted by additive white noise with performance prediction /V. Naumenko, V. Lukin, S. Krivenko// Proceedings of ICTM 2021. 2021.
Zhong, P. Multiple-spectral-band CRFs for denoising junk bands of hyper-spectral imagery /P. Zhong, R. Wang// IEEE Transactions on Geoscience and Remote Sensingю 2013. Vol.51, No. 4. P. 2269–2275.
Chatterjee, P. Is Denoising Dead? /P. Chatterjee, P. Milanfar// IEEE Trans. Image Process. 2010. Vol. 19. P. 895–911. doi:10.1109/TIP. 2009.2037087.
Nonparametric Multiscale Blind Estimation of Intensity-Frequency-Dependent Noise /M. Colom, M. Lebrun, A. Buades, J. Morel// IEEE Transactions on Image Processing. 2015. Vol. 24. P. 3162-3175. doi: 10.1109/TIP.2015.2438537.
Selva, E. K-Means Based Blind Noise Variance Estimation / E. Selva, A. Kountouris, Y. Louet// 2021 IEEE 93rd Vehicular Technology Conference, VTC2021. Spring, 2021. P. 1-7. DOI: 10.1109/VTC2021-Spring51267.2021.9449072.
Analysis of HVS-Metrics Properties Using Color Image Database TID2013 /V. Lukin, N. Ponomarenko, K. Egiazarian, J. Astola// Proceedings of ACIVS. - Italy. - October 2015. - P. 613-624.
Lossy compression of images without visible distortions and its applications /V. Lukin, M. Zriakhov, S. Krivenko, N. Ponomarenko, Z. Miao// Proceedings of ICSP. Bei-jing. 2010. P. 694-697.
On between-coefficient contrast masking of DCT basis functions /N. Ponomarenko, F. Silvestri, K. Egiazarian, M. Carli, J. Astola, V. Lukin// Proceeding of the Third International Workshop on Video Processing and Quality Metrics for Consumer Electronics (VPQM). Scottsdale, AZ, USA. 25-26 January 2007. p. 4.
Wang, Z. Multiscale structural similarity for image quality assessment / Z. Wang, E. P. Simoncelli, A. C. Bovik// IEEE Asilomar Conference on Signals, Systems and Computers. 2003. Vol. 2. P. 1398-1402. DOI: 10.1109/ ACSSC.2003.1292216.
Wei, Z. Spatio-Temporal Just Noticeable Distortion Profile for Grey Scale Image/Video in DCT Domain /Z. Wei, K. N. Ngan// IEEE Transactions on Circuits and Systems for Video Technology. 2009. Vol.19, No.3. P. 337-346. DOI: 10.1109/ TCSVT.2009.2013518.
Prediction of filtering efficiency for DCT-based image denoising /S. Abramov, S. Krivenko, A. Roenko, V. Lukin, I. Djurović, M. Chobanu// 2013 2nd Mediterranean Conference on Embedded Computing (MECO). 2013. P. 97-100. doi: 10.1109/ MECO.2013.6601327.
Cameron, A.C. An R-squared measure of goodness of fit for some common nonlinear regression models /A.C. Cameron, F. Windmeijer// Journal of Econometrics. 1997. Vol. 77, No. 2. P. 329-342. doi: 10.1055/s-0028-1109341.
Guruswami, V. Robust Fourier and Polynomial Curve Fitting /V. Guruswami, D. Zuckerman// 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS). 2016. P. 751-759. doi:10.31838/jcr.07.05.130.
Schowengerdt, R. Remote Sensing: Models and Methods for Image Processing /R. Schowengerdt. Academic Press Publ. 2006. 506 p.
Duda, R.O. Pattern Classification, 2nd ed. /R.O. Duda, P.E. Hart, D.G. Stork// New York, Wiley. 2000.
Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area /V. Lukin, O. Rubel, R, Kozhemiakin, S. Abramov, A,Shelestov, M. Lavreniuk, M. Meretsky, B. Vozel, K. Chehdi// Classification Book chapter in "Recent Advances and Applications in Remote Sensing," edited by Dr. Ming-Chih Hung. InTech. 2018. P. 21-40.
Congalton, R. Accuracy assessment and validation of remotely sensed and other spatial information /R. Congalton. International Journal of Wildland Fire. 2001. Vol. 10. P. 321 – 328.
Congalton, R.G. Assessing the accuracy of remotely sensed data: principles and practices /R.G. Congalton, K. Green// Mapping science series; Lewis Publications. Boca Raton. 1999. ISBN 978-0-87371-986-5.
Stotts, L.B. Signal detection and estimation theory /L.B. Stotts. Free Space Optical Systems Engineering: Design and Analysis. NY: Wiley Telecom. 2017.
Vasilyeva, I. Methods for Predicting Multichannel Images Classification Efficiency /I. Vasilyeva, V. Lukin// IEEE Int. Conf. on Problems of Infocommunications. Science and Technology (PIC S&T). 2020. P. 101 – 106.
Analysis of Classification Quality of DAT-Based Compression Images /G. Proskura, V. Makarichev, O. Rubel, V. Lukin// Proceedings of TCSET 2022. Lviv, Ukraine. February 2022. P. 233 - 238.
Васильева, И.К. Анализ методов постклассификационной обработки многоканальных изображений /И.К. Васильева, В.В. Лукин// Радиоэлектронные и компьютерные системы. Харьков, ХАИ. 2019. №1(89). С. 17-28.
Fishman, G.S. Monte Carlo: Concepts, Algorithms, and Applications /G.S. Fishman. Springer-Verlag, New York. 1996.
Fukunaga, K. Effects of sample size in classifier design / K. Fukunaga, R.R. Hayes// IEEE Transactions on Pattern Analysis and Machine Intelligence. Aug. 1989. Vol. 11, No. 8. P. 873 – 885.
Yang, Y.L. A research on classification performance of fuzzy classifiers based on fuzzy set theory /Y.L. Yang, X.Y. Bai// Iranian Journal of Fuzzy Systems. 2019. Vol. 16. P. 15 – 27.
Estimation of sensor noise parameters from remote sensing data using convolutional neural networks and mixed synthetic and real data /M. Uss, B. Vozel, S. Abramov, V. Lukin// accepted to ISSOIA Conference. China. Nov. 2022. 20 p.
Optimal Operation Point Existence and Its Parameters in BPG-Based Automatic Lossy Compression of Noisy Images /V. Lukin, B. Kovalenko, S. Kryvenko, V. Naumenko, B. Vozel// Current Overview on Science and Technology Research. Publisher: B P International. November 2022. Vol. 9. 36 p. DOI: 10.9734/bpi/costr/v9/4316A.
Kovalenko, B. BPG-Based Lossy Compression of Three-channel Noisy Images with Prediction of Optimal Operation Existence and Its Parameters /B. Kovalenko, V. Lukin, B. Vozel// Remote Sensing 15. 2023. no. 6: 1669. https://doi.org/ 10.3390/rs15061669.
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