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Image Denoising and Various Techniques: Survey

Rashmi Priya, Yogesh Kumar, Kushagra Goel

Abstract


Whenever an image is formed, various factors like lightning which includes its source, distance, intensity, and the camera characteristics, which included the sensor responses, lenses, and so on, affect the overall appearance of the image. The noise is one of the key components in this. It is important to remove noise in such a way that the overall quality is maintained and there is no loss of image information. There are several noise limitation strategies in coloration image processing, and these may be decided on the basis of the clamor distortion in the photo. For denoising of picture, one wishes to have a near eye for mining of pictures, segregation and segmentation, sample reputation, and hence the pleasant method scan be implemented.


Keywords


Speckle noise, algorithm, salt and pepper noise,Speckle noise, algorithm, salt and pepper noise

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References


Jabarullah BM, Saxena S, Babu D. Survey on noise removal in digital images. IOSR J Comput Eng. 2012; 6 (4): 45–51. doi: 10.9790/0661-0644551.

Yang MH, Kriegman DJ, Ahuja N. Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell. 2002; 24 (1): 34–58. doi: 10.1109/34.982883.

Liu C, Szeliski R, Bing Kang SB, Zitnick CL, Freeman WT. Automatic estimation and removal of noise from a single image. IEEE Trans Pattern Anal Mach Intell. 2008; 30 (2): 299–314. doi: 10.1109/TPAMI.2007.1176, PMID 18084060.

Singh A. Noise reduction of an image from non local means value algorithm. Dissertation synopsis for image denoising (noise reduction) using non local mean algorithm; 2017. Available at https://www.slideshare.net/artis9/dissertation-synopsis-for-imagedenoisingnoise-reduction-using-non-local-mean-algorithm [Accessed on February 2023]

Farooque MA, Rohankar JS. Survey on various noises and techniques for denoising the color image. Int J Appl Innov Eng Mgmt. 2013; 2 (11): 217–221.

Owotogbe JS, Ibiyemi TS, Adu BA. A comprehensive review on various types of noise in image processing. Int J Sci Eng Res. 2019; 10 (11): 388–393.

Vijaykumar VR, Vanathi PT, Kanagasapathy P. Adaptive window based efficient algorithm for removing Gaussian noise in gray scale and color images. In: International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007). Vol. 3. IEEE Publications; 2007 Dec 13. doi: 10.1109/ICCIMA.2007.367.

Nguyen TA, Song WS, Hong MC. Spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise. IEEE Trans Con Electron. 2010 Oct 28; 56 (3): 1610–1615. doi: 10.1109/TCE.2010.5606304.

Agarwal R. Bit plane average filtering to remove Gaussian noise from high contrast images. In: 2012 International Conference on Computer Communication and Informatics 2012 Jan 10. IEEE Publications. p. 1–5. doi: 10.1109/ICCCI.2012.6158801.

Rakesh Singh, Amandeep Kaur, ”Comparative Analysis of Speckle Noise Reduction Techniques and their affect on Image Edge Locaization”, IJCST, Vol.2 Issue 4, Oct-Dec 2011,pp 78-82.

Sreekanth Rao T, Gangamohan P, Nagarjuna Reddy P, Prathyusha B. Wavelet based image de-noising of non logarithmic transformed data. Int J Comput Sci Technol. 2011; 2 (SP1): 213–215. Available at http://www.ijcst.com/icaccbie11/sp1/sreekanth.pdf [Accessed on February 2023]

Ganesh L, Chaitanya SK, Rao JD, Kumar MN. Development of image fusion algorithm for impulse noise removal in digital images using the quality assessment in spatial domain. Int J Eng Res Appl. 2014; 1: 786–792.

Zhu R, Wang Y. Application of improved median filter on image processing. J Comput. 2012; 7 (4): 838–841. doi: 10.4304/jcp.7.4.838-841.

Gupta MM. Fuzzy neural networks: theory and applications. In: Intelligent robots and computer vision XIII: Algorithms and computer vision. Vol. 2353. SPIE; 1994 Oct 10. Available at https://ui.adsabs.harvard.edu/abs/1994SPIE.2353..303G/abstract [Accessed on February 2023]

Iyyappan MS, Nandagopal MV. Automatic accident detection and ambulance rescue with intelligent traffic light system. Int J Adv Res Electr Electron Instrum Eng. 2013; 2 (4): 1319–1325.

Padzil FM. (2016). Linear and nonlinear filter for image processing using matlab’s image processing toolbox. [Online] Available from:at https://researchrepository.murdoch.edu.au/id/eprint/30815/1/whole.pdf [Accessed on February 2023]

Khalid N, Ahmed B, Gameraddin M, Yousef M. Sonographic Measurement of Fetal Kidney Length as Parameter for Fetal Weight Estimation for Sudanese Population. Int J Sci Res. 2016; 5 (4): 2319–7064.

Tin HH. Removal of noise by median filtering in image processing. Conference: The 6th Parallel and Soft Computing (PSC 2011); December 2011; Yangon, Myanmar. Available at https://www.researchgate.net/publication/263547654_Removal_of_Noise_Reduction_for_Image_Processing [Accessed on February 2023]

Jagadish JS, Doshi TD. A study on bracing systems on high rise steel structures. Int J Eng Res Technol. 2013; 2: 1672–1676.

El-Hussein K, Fourier transform and Plancherel formula for Galilean Group (Spacetime). International J App Innovation in Eng Mgmt. 2013; 2 (11): 195–200.


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