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Telecommunications and Radio Engineering

Publicou 12 edições por ano

ISSN Imprimir: 0040-2508

ISSN On-line: 1943-6009

SJR: 0.185 SNIP: 0.268 CiteScore™:: 1.5 H-Index: 22

Indexed in

Image Filtering Based on Discrete Cosine Transform

Volume 66, Edição 18, 2007, pp. 1685-1701
DOI: 10.1615/TelecomRadEng.v66.i18.70
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RESUMO

Quite often acquired images are noisy and filtering is a standard operation to improve image quality. "Good" filters should preserve details, edges and texture features and, at the same time, suppress noise. Recently it has been demonstrated that transform based filters are able to satisfy these requirements well enough and much attention has been paid to wavelet based filter design. In this paper it is shown that another kind of transform based filters, namely, discrete cosine transform (DCT) filters is an effective tool that competes with the state of the art methods. An additive noise case is basically considered although quite easily the DCT based filter can be modified for coping with other types of noise.

CITADO POR
  1. Lukin Vladimir V., Discrete cosine transform–based local adaptive filtering of images corrupted by nonstationary noise, Journal of Electronic Imaging, 19, 2, 2010. Crossref

  2. Lukin Vladimir V., Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics, Journal of Applied Remote Sensing, 5, 1, 2011. Crossref

  3. Fevralev Dmitriy V, Ponomarenko Nikolay N, Lukin Vladimir V, Abramov Sergey K, Egiazarian Karen O, Astola Jaakko T, Efficiency analysis of color image filtering, EURASIP Journal on Advances in Signal Processing, 2011, 1, 2011. Crossref

  4. Lukin Vladimir, Abramov Sergey, Krivenko Sergey, Kurekin Andriy, Pogrebnyak Oleksiy, Analysis of classification accuracy for pre-filtered multichannel remote sensing data, Expert Systems with Applications, 40, 16, 2013. Crossref

  5. Rubel Oleksii S., Lukin Vladimir V., De Medeiros Fatima S., Prediction of Despeckling Efficiency of DCT-Based Filters Applied to SAR Images, 2015 International Conference on Distributed Computing in Sensor Systems, 2015. Crossref

  6. Krivenko Sergey, Lukin Vladimir, Vozel Benoit, Chehdi Kacem, Prediction of DCT-based denoising efficiency for images corrupted by signal-dependent noise, 2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO), 2014. Crossref

  7. Rubel Oleksii S., Kozhemiakin Ruslan O., Krivenko Sergey S., Lukin Vladimir V., Vozel Benoit, Chehdi Kacem, A method for predicting denoising efficiency for color images, 2015 IEEE 35th International Conference on Electronics and Nanotechnology (ELNANO), 2015. Crossref

  8. Lukin Vladimir, Abramov Sergey, Ponomarenko Nikolay, Egiazarian Karen, Astola Jaakko, Image filtering: Potential efficiency and current problems, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011. Crossref

  9. Ponomarenko N. N., Lukin V. V., Zriakhov M. S., Kaarna A., Astola J., Automatic Approaches to On-Land/On-Board Filtering and Lossy Compression of AVIRIS Images, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008. Crossref

  10. Rubel Aleksey, Naumenko Aleksey, Lukin Vladimir, A neural network based predictor of filtering efficiency for image enhancement, 2014 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS), 2014. Crossref

  11. Uss Mikhail, Rubel Aleksey, Lukin Vladimir, Vozel Benoit, Chehdi Kacem, Lower bound on image filtering mean squared error in the presence of spatially correlated noise, 2014 IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS), 2014. Crossref

  12. Rubel Aleksey, Lukin Vladimir, Uss Mikhail, Vozel Benoit, Pogrebnyak Oleksiy, Egiazarian Karen, Efficiency of texture image enhancement by DCT-based filtering, Neurocomputing, 175, 2016. Crossref

  13. Lukin V.V., Krivenko S.S., Zriakhov M.S., Ponomarenko N.N., Abramov S.K., Kaarna A., Egiazarian K., Lossy compression of images corrupted by mixed Poisson and additive Gaussian noise, 2009 International Workshop on Local and Non-Local Approximation in Image Processing, 2009. Crossref

  14. Ponomarenko Nikolay, Lukin Vladimir, Egiazarian Karen, HVS-metric-based performance analysis of image denoising algorithms, 3rd European Workshop on Visual Information Processing, 2011. Crossref

  15. Rubel Oleksii, Lukin Vladimir, Egiazarian Karen, On prediction of DCT-based denoising efficiency under spatially correlated noise conditions, 2016 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 2016. Crossref

  16. Stanković Isidora, Orović Irena, Daković Miloš, Stanković Srdjan, Denoising of sparse images in impulsive disturbance environment, Multimedia Tools and Applications, 77, 5, 2018. Crossref

  17. Rubel Oleksii, Lukin Vladimir, Abramov Sergey, Vozel Benoit, Pogrebnyak Oleksiy, Egiazarian Karen, Is Texture Denoising Efficiency Predictable?, International Journal of Pattern Recognition and Artificial Intelligence, 32, 01, 2018. Crossref

  18. Lukin Vladimir, Ponomarenko Nikolay, Kurekin Andrey, Pogrebnyak Oleksiy, Processing and Classification of Multichannel Remote Sensing Data, in Advances in Soft Computing, 7095, 2011. Crossref

  19. Boukhechba Kamel, Wu Huayi, Bazine Razika, DCT-Based Preprocessing Approach for ICA in Hyperspectral Data Analysis, Sensors, 18, 4, 2018. Crossref

  20. Rubel Andrey, Lukin Vladimir, Regression-based analysis of visual quality for denoised images, 2017 4th International Scientific-Practical Conference Problems of Infocommunications. Science and Technology (PIC S&T), 2017. Crossref

  21. Egiazarian Karen O., Agaian Sos S., Gotchev Atanas P., Rubel Aleksey S., Lukin Vladimir V., Egiazarian Karen O., A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise, Image Processing: Algorithms and Systems XIII, 9399, 2015. Crossref

  22. Lukin Vladimir, Processing of Multichannel Remote Sensing Data for Environment Monitoring, in GeoSpatial Visual Analytics, 2009. Crossref

  23. Rubel Aleksey, Lukin Vladimir, Pogrebnyak Oleksiy, Efficiency of DCT-Based Denoising Techniques Applied to Texture Images, in Pattern Recognition, 8495, 2014. Crossref

  24. Bazine Razika, Wu Huayi, Boukhechba Kamel, Spatial Filtering in DCT Domain-Based Frameworks for Hyperspectral Imagery Classification, Remote Sensing, 11, 12, 2019. Crossref

  25. Bazine Razika, Wu Huayi, Boukhechba Kamel, Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification, Remote Sensing, 11, 24, 2019. Crossref

  26. Lukin Vladimir, Abramov Sergey, Kozhemiakin Ruslan, Rubel Alexey, Uss Mikhail, Ponomarenko Nikolay, Abramova Victoriya, Vozel Benoit, Chehdi Kacem, Egiazarian Karen, Astola Jaakko, DCT-Based Color Image Denoising: Efficiency Analysis and Prediction, in Color Image and Video Enhancement, 2015. Crossref

  27. Rubel A. S., Lukin V. V., Assessment of visual quality of denoised images, Information extraction and processing, 2018, 46, 2018. Crossref

  28. Rubel Andrey, Lukin Vladimir, Denoising efficiency analysis based on no-reference image quality assessment, 2018 14th International Conference on Advanced Trends in Radioelecrtronics, Telecommunications and Computer Engineering (TCSET), 2018. Crossref

  29. Ponomarenko Mykola, Bahnemiri Sheyda Ghanbaralizadeh, Egiazarian Karen, Deep Convolutional Network for Spatially Correlated RAYLEIGH Noise Suppression on TerraSAR-X Images, 2020 IEEE Ukrainian Microwave Week (UkrMW), 2020. Crossref

  30. Zemliachenko Alexander, Lukin Vladimir, Djurovic Igor, Vozel Benoit, On potential to improve DCT-based denoising with local threshold, 2018 7th Mediterranean Conference on Embedded Computing (MECO), 2018. Crossref

  31. Hua Tuan, Li Qingyu, Dai Keren, Zhang Xiangjin, Zhang He, Image denoising via neighborhood-based multidimensional Gaussian process regression, Signal, Image and Video Processing, 2022. Crossref

  32. Volosyuk Valeriy, Zhyla Simeon, Inkarbaieva Olha, Kolesnikov Denys, Optimization of the Surface Formation Algorithm by the Airborne Helicopter Radar, 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), 2022. Crossref

  33. Bahnemiri Sheyda Ghanbaralizadeh, Ponomarenko Mykola, Egiazarian Karen, Learning-Based Noise Component Map Estimation for Image Denoising, IEEE Signal Processing Letters, 29, 2022. Crossref

  34. Wang Decheng, Zhao Feng, Yi Hui, Li Yinan, Chen Xiangning, An unsupervised heterogeneous change detection method based on image translation network and post-processing algorithm, International Journal of Digital Earth, 15, 1, 2022. Crossref

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