An Improved feature extraction model for retrieval Images from various noise image sources by the environmental effects
Résumé
The purpose of image processing is to transform an image into various digital forms using multiple procedures and to get an improved appearance or retrieval image from different sorts of image sources, such as space probes, satellites, and photos in a day-to-day environment application development with trustworthy data presentation under adverse weather conditions, such as rain, snow, and fog. Diverse aspects, such as ecological situations during the picture selection and the quality of sensing elements themselves, change the efficiency of imaging sensors. This paper proposes an improved new algorithm model to extract images from various noisy image sources and characterize the haze and fog effects for different targets.
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Références
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International Conference on Micro-Electronics and Telecommunication Engineering, IEEE Xplore, 2016.
[34]https://udayton.edu/engineering/research/centers/vision_lab/research/video_preprocessing/fog_haze_removal.php.
[02] Shizhe Zang; Ming Ding; David Smith; Paul Tyler; Thierry Rakotoarivelo; Mohamed Ali Kaafar “The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car,” IEEE Vehicular Technology Magazine, Issue 2, June-2019.
[03] Yuxiao Zhang, Alexander Carballo, Hanting Yang, Kazuya Takeda, “Perception and sensing for autonomous vehicles under adverse weather conditions: A survey.” ELSEVIER-ISPRS Journal of Photogrammetry and Remote Sensing, December 2022.
[04] ChongyiLi, ChunleGuo, Linghao Han, Jun Jiang, Ming-Ming Cheng , Senior Member, IEEE, Jinwei Gu and Chen Change Loy “Low-Light Image and Video Enhancement Using Deep Learning: A Survey.” IEEE Transaction on Pattern Analysisand Machine Intelligence, Vol. 44, No. 12, December 2022.
[05] Subhash Chand Agrawal, Anand Singh Jalal, “A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images,” Journal of Visual Communication and Image RepresentationVolume 77, May 2021.
[06] Kanchan Lakra, Kirti Avishek, “A review on factors influencing fog formation, classification, forecasting, detection and impacts,” Rendiconti Lincei. Scienze Fisiche eNaturali, Volume 33, pages, 319–353 (2022).
[07] Yangyang Li, Cheng Peng, Yanqiao Chen, Licheng Jiao, Linhao Zhou, and Ronghua Shang, “A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images,” IEEE Transaction on Geosciences and remote sensing, Vol, 57, No. 8, August 2019.
[08] S. Grace Chang, Bin Yu and Martin Vetterli, “Adaptive Wavelet Thresholding for Image Denoising and Compression,” IEEE Transaction on Image Processing, Vol. 9, No. 9, September 2000.I
[09] Qingkun Song, Li Ma, JianKun Cao, Xiao Han, “Image Denoising Based on Mean Filter and Wavelet Transform,” IEEE Xplore International Conference on Advanced Information Technology and Sensor Application, August, 2015.
[10] L. Sherlin Pravina, S. Shunmugan, “Survey on Image Denoising Methods,” Proceedings of the Fourth International Conference on I-SMAC, IEEE Xplore.
[11] Fayaz Ali Dharejo, Yuanchun Zhou, Farah Deeba, Munsif Ali Jatoi, Yi Du and Xuezhi Wang, “A remote-sensing image enhancement algorithm based on patch-wise dark channel prior and histogram equalization with color correction,” IET Image Processing, Wiley, August, 2020.
[12] DikshaAdke; AtharvaKarnik; Honey Berman;
ShyamalaMathi, “Detection and Blur-Removal of Single Motion Blurred Image using Deep Convolutional Neural Network”,International Conference on Artificial Intelligence and Computer Science Technology (ICAICST)IEEE Xplore: 30 July 2021.
[13] Blind Deconvolution Algorithm-MatWorks.
[14] Qian Jiang, Xin Jin, Shin-Jye Lee, and Shaowen Yao, “A Novel Multi-Focus Image Fusion Method Based on Stationary Wavelet Transform and Local Features of Fuzzy Sets.” IEEE Access.
[15] Linwei Fan, Fan Zhang, Hui Fan & Caiming Zhang, “Brief review of image denoising techniques” Visual Computing for Industry, Biomedicine, and Art, volume 2, Article number: 7 (2019).
[16] Kyungil Kim, Soohyun Kim, Kyung-Soo Kim
“Effective image enhancement techniques for fog-affected indoor and outdoor images”, IET Image Processing, 01 April 2018.
[17] Edwin H. Land and John J. McCann, “Lightness and Retinex Theory,” Journal of the Optical Society of America”, Vol. 61,Issue 1,pp. 1-11(1971).
[18] Stephen M. Pizer, E. Philip Amburn, etl, “Adaptive histogram equalization and its variations”, Computer Vision, Graphics, and Image Processing, Volume 39, Issue 3, September 1987, Pages 355-368.
[19]J.A. Stark, “Adaptive image contrast enhancement using generalizations of histogram equalization”, IEEE Transactions on Image Processing, Issue 5 • May-2000.
[20] Johannes Kopf, Boris Neubert, etl, “Deep photo: model-based photograph enhancement and viewing”, ACM Transactions on Graphics, Vol. 27, No. 5.
[21] Raanan Fattal, “Single image dehazing”, ACM Transactions on Graphics, Vol. 27, No. 3.
[22] Kaiming He; Jian Sun; Xiaoou Tang “Single Image Haze Removal Using Dark Channel Prior”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 12 • Dec.-2011.
[23] Kaiming He; Jian Sun; Xiaoou Tang, “Guided Image Filtering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 6, June-2013.
[24] Garima Yadav, Saurabh Maheshwari, “Fog removal techniques from images_ A comparative review and future directions” 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT)-IEE Xplore.
[25] Umme Sara1, Morium Akter2, Mohammad Shorif Uddin2, “Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study”, Journal of Computer and Communications, Vol.7 No.3, March 2019.
[26] Mayana Shah, Dr. U. D. Dalal, “SDME Quality Measure based Stopping Criteria for Iterative Deblurring Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 10, 2016.
[27] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity”, IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.
[28] S. Agaian, B. Panetta, and Y. Zhou, “Nonlinear Unsharp Masking for Mammogram Enhancement,” IEEE Trans. Information Technology in Biomedicine, vol. 15, no. 6, pp. 918–928, 2011.
[29] S. Delmarco and S. Agaian, "The design of wavelets for imageenhancement and target detection," Proceedings of SPIE, vol. 7351, 2009.
[30] K. Panetta, Y. Zhou, and S. Agaian, "Nonlinear Unsharp Masking for Mammogram Enhancement," IEEE Transactions on Information Technology in Biomedicine, vol. 15, pp. 918-928,2011.
[31] M. J. Shah and Upena Dalal, “3D-Image Restoration Technique Using Genetic Algorithm to Solve Blurring Problems of Images” Imaging Science Journal, Volume 62, No. 07, pp.365-374, sept. 2014.
[32] T. Maha Lakshmi, Dr. Alluri Sreenivas, “An Efficient Image Noise Estimation Technique Using Type-2 Fuzzy Set and Genetic Algorithm”. Journal of Theoretical and Applied Information Technology 31st July 2017. Vol.95. No 14, 2005.
[33] Dr.PK Chaturvedi Dr. Ritu Vijay R.D. Nirala “Visual Improvement for Dense foggy & hazy weather images, Using multimodal enhancement techniques”.
International Conference on Micro-Electronics and Telecommunication Engineering, IEEE Xplore, 2016.
[34]https://udayton.edu/engineering/research/centers/vision_lab/research/video_preprocessing/fog_haze_removal.php.
Publiée
2025-12-21
Rubrique
Mathematics and Computing - Innovations and Applications (ICMSC-2025)
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