Detection of Noise in Digital Images by Using the Averaging Filter Name COV

One of the significant problems in digital signal processing is the filtering and reduction of undesired interference. Due to the abundance of methods and algorithms for processing signals characterized by complexity and effectiveness of removing noise from a signal, depending on the character and level of noise, it is difficult to choose the most effective method. So long as there is specific knowledge or grounds for certain assumptions as to the nature and form of the noise, it is possible to select the appropriate filtering method so as to ensure optimum quality. This chapter describes several methods for estimating the level of noise and presents a new method based on the properties of the smoothing filter.

[1]  Paul L. Rosin Thresholding for change detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[2]  Aggelos K. Katsaggelos,et al.  Digital image restoration , 2012, IEEE Signal Process. Mag..

[3]  Fionn Murtagh,et al.  Image processing through multiscale analysis and measurement noise modeling , 2000, Stat. Comput..

[4]  Grzegorz Mikolajczak,et al.  Differential Approximation of the 2-D Laplace Operator for Edge Detection in Digital Images , 2010, ICCCI.

[5]  Søren I. Olsen,et al.  Estimation of Noise in Images: An Evaluation , 1993, CVGIP Graph. Model. Image Process..

[6]  Jong-Sen Lee,et al.  Refined filtering of image noise using local statistics , 1981 .

[7]  Grzegorz Mikolajczak,et al.  Generation of FIR filters by using neural networks to improve digital images , 2011, 2011 34th International Conference on Telecommunications and Signal Processing (TSP).

[8]  Grzegorz Mikolajczak,et al.  The Synchronization of the Images Based on Normalized Mean Square Error Algorithm , 2010, MISSI.

[9]  S. Holland MRI: Acceptance testing and quality control—The role of the clinical medical physicist , 1989 .

[10]  Azriel Rosenfeld,et al.  A Fast Parallel Algorithm for Blind Estimation of Noise Variance , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  H. Schroder,et al.  A new video noise reduction algorithm using spatial subbands , 1996, Proceedings of Third International Conference on Electronics, Circuits, and Systems.

[12]  Gary Mastin,et al.  Adaptive filters for digital image noise smoothing: An evaluation , 1985, Comput. Vis. Graph. Image Process..

[13]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Tadeusz M. Szuba,et al.  Computational Collective Intelligence , 2001, Lecture Notes in Computer Science.