International Journal of Advanced Technology and Engineering Exploration (IJATEE) ISSN (P): 2394-5443 ISSN (O): 2394-7454 Vol - 5, Issue - 43, June 2018
  1. 1
    Google Scholar
A survey on impulse noise removal techniques in image processing

Baby Victoria L. and Sathappan S.

Abstract

In image processing, an essential and most challenging process is removing the noise from the color images. Images are often corrupted by impulse noise during image acquisition and transmission. Therefore, impulse noise reduction is the most crucial aspect during image transmission. Over the past decades, several approaches have been proposed for removing the impulse noise from the images in such a way that the most significant information of the images is preserved. Hence, the original image quality can be restored efficiently. This paper presents a detailed survey of impulse noise removal techniques. Initially, different techniques are analysed and its limitations are addressed. Moreover, performance of all techniques was compared to identify their effectiveness for further improvement on impulse noise removal techniques. Finally, some future contributions are also provided to improve the impulse noise removal techniques significantly.

Keyword

Image processing, Impulse noise, Noise removal, Image restoration.

Cite this article

Refference

[1][1]Davis RR, Clavier O. Impulsive noise: a brief review. Hearing Research. 2017; 349:34-6.

[2][2]Koli M, Balaji S. Literature survey on impulse noise reduction. Signal & Image Processing. 2013; 4(5):75-95.

[3][3]Suganthi A, Senthilmurugan M. Comparative study of various impulse noise reduction techniques. International Journal of Engineering Research and Application. 2013; 3(5):1302-6.

[4][4]Pritamdas K, Singh KM, Singh LL. A summary on various impulse noise removal techniques. International Journal of Science and Research. 2017; 6(3):941-54.

[5][5]Gupta V, Chaurasia V, Shandilya M. Random-valued impulse noise removal using adaptive dual threshold median filter. Journal of Visual Communication and Image Representation. 2015; 26:296-304.

[6][6]Chen CL, Liu L, Chen L, Tang YY, Zhou Y. Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Transactions on Image Processing. 2015; 24(11):4014-26.

[7][7]Wang R, Pakleppa M, Trucco E. Low-rank prior in single patches for nonpointwise impulse noise removal. IEEE Transactions on Image Processing. 2015; 24(5):1485-96.

[8][8]Wang X, Shi G, Zhang P, Wu J, Li F, Wang Y, et al. High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution. IET Image Processing. 2016; 10(4):304-13.

[9][9]Majumdar A, Ansari N, Aggarwal H, Biyani P. Impulse denoising for hyper-spectral images: a blind compressed sensing approach. Signal Processing. 2016; 119:136-41.

[10][10]Roig B, Estruch VD. Localised rank-ordered differences vector filter for suppression of high-density impulse noise in colour images. IET Image Processing. 2016; 10(1):24-33.

[11][11]Jin L, Zhu Z, Xu X, Li X. Two-stage quaternion switching vector filter for color impulse noise removal. Signal Processing. 2016; 128:171-85.

[12][12]Roy A, Singha J, Manam L, Laskar RH. Combination of adaptive vector median filter and weighted mean filter for removal of high-density impulse noise from colour images. IET Image Processing. 2017; 11(6):352-61.

[13][13]Veerakumar T, Subudhi BN, Esakkirajan S, Pradhan PK. Context model based edge preservation filter for impulse noise removal. Expert Systems with Applications. 2017; 88:29-44.

[14][14]Xu S, Yang X, Jiang S. A fast nonlocally centralized sparse representation algorithm for image denoising. Signal Processing. 2017; 131:99-112.

[15][15]Jin KH, Ye JC. Sparse and low-rank decomposition of a hankel structured matrix for impulse noise removal. IEEE Transactions on Image Processing. 2018; 27(3):1448-61.