literature.pptx
- 1. A Fast Two-Dimensional Median Filtering Algorithm Huang, T., G. J. T. G. Y. Yang, and Greory Tang. "A fast two-dimensional median filtering algorithm." IEEE Transactions on Acoustics, Speech, and Signal Processing 27.1 (1979): 13-18. From one output picture element to the next, the m X n window moves only one column. To get the numbers in the new window from those in the preceding window, we throw away n points and add in n new points. The remaining mn-2n numbers are unchanged. For a window size of m X n, the computer time required is O(n). Only software algorithm is implemented. There didn’t implemented hardware circuit.
- 2. A new directional weighted median filter for removal of random-valued impulse noise. Dong, Yiqiu, and Shufang Xu. "A new directional weighted median filter for removal of random-valued impulse noise." IEEE signal processing letters 14.3 (2007): 193-196. A new impulse detector is proposed, which is based on the differences between the current pixel and its neighbors aligned with four main directions. Then, we combine it with the weighted median filter to get a new directional weighted median (DWM) filter. it can preserve edges very well, even thin lines, as removing noise It works poorly for highly corrupted images only.
- 3. Center Weighted Median Filters and Their Applications to Image Enhancement preserving smoother that can suppress additive white, impulsive, and multiplicative noise multistage median filters, and Winsorizing smoothers were pointed out. Ko, S. J., & Lee, Y. H. (1991). Center weighted median filters and their applications to image enhancement. IEEE transactions on circuits and systems, 38(9), 984-993.
- 4. Parallel VLSI Design for a Real-Time Video-Impulse Noise-Reduction Processor The proposed algorithm is split into two parts for impulse noise removal, i.e., noise detection (ND) and adaptive filter. With differential computation consisting of pipelined architecture, hardware efficiency can be boosted. In combination with extra components, such as an A/D converter and a D/A converter, this chip can remove impulse noise in real time for current TV systems. Hsia, S. C. (2003). Parallel VLSI design for a real-time video-impulse noise-reduction processor. IEEE transactions on very large scale integration (VLSI) systems, 11(4), 651-658.
- 5. Iterative Relaxed Median Filter for Impulse Noise Removal and Validation of FCM Clustering Using Cluster Error Index in Median Filtered MR Images IRMF results in improved noise removal in terms of PSNR than other methods but not preservation of edges. Even though adaptive median filtering is good for other imageries, it shows poor performance for the image taken for analysis. Quality of a partition provided by clustering algorithms is evaluated by a function called Cluster Error Index (CEI). Vijayarajan, R., and S. Muttan. "Iterative Relaxed Median Filter for Impulse Noise Removal and Validation of FCM Clustering Using Cluster Error Index in Median Filtered MR Images." International Conference on Computing and Communication Systems. Springer, Berlin, Heidelberg, 2011.
- 6. Digital Image Smoothing and the Sigma Filter (1) Establish an intensity range (xi, j + A, xi, - A), where A = 2sigma. (2) Sum all pixels which lie within the intensity range in a (2n + 1,2m + 1) window. (3) Compute the average by dividing the sum by the number of pixels in the sum. (4) Then R;, j = the average. The value of K should be carefully chosen to remove isolated spot noise without destroying thin features and subtle details. For a 7 x 7 window, K should be less than 4, and it should be less than 3 for a 5 X 5 window. Lee, Jong-Sen. "Digital image smoothing and the sigma filter." Computer vision, graphics, and image processing 24.2 (1983): 255-269.
- 7. An Efficient Edge-Preserving Algorithm for Removal of Salt-and-Pepper Noise R(i,j)=Median(f(i,j),b,d,e,g) Chen, Pei-Yin, and Chih-Yuan Lien. "An efficient edge-preserving algorithm for removal of salt-and-pepper noise." IEEE Signal Processing Letters 15 (2008): 833-836.
- 8. An Efficient Denoising Architecture for Removal of Impulse Noise in Images Lien, Chih-Yuan, et al. "An efficient denoising architecture for removal of impulse noise in images." IEEE Transactions on computers 62.4 (2012): 631-643.
- 9. Novel Hardware Implementation of Adaptive Median Filters Y(I,j)=x(med) if x(min)<x(med)<x(max) Vasicek, Zdenek, and Lukas Sekanina. "Novel hardware implementation of adaptive median filters." 2008 11th IEEE Workshop on Design and Diagnostics of Electronic Circuits and Systems. IEEE, 2008.
- 10. A Decision Based Unsymmetrical Trimmed Variants for the Removal of High Density Salt and Pepper Noise Step 1: Choose 2-D window of size 3x3. The processed pixel in current window is assumed as pxy. Step 2: Check for the condition 0 < pxy < 255, if the condition is true then pixel is considered as not noisy and left unaltered. Step 3: If the processed pixel pxy holds 0 or 255 i.e. (pxy=0 or pxy =255) then pixel pxy is considered as corrupted pixel. Convert 2D array into 1D array. Sort the 1D array which is assumed as Sxy. Step 4: Initialize two counters, forward counter (F) and reverse counter (L) with 1 and 9 respectively. When a 0 or 255 is encountered inside the window F is increased by 1 or L is decremented by 1 respectively. When pixel is noisy there happens to be two possible cases. Check for the number of corrupted pixel inside the current processing window. It is denoted as count. Case 1) if the count value <=3 inside the current processing window then the corrupted pixel is replaced with median of unsymmetrical trimmed output. Case 2) if the count value is greater than 3 then the noisy pixel is replaced with mean of Fth and Lth value of the rank ordered unsymmetrical trimmed output. If entire pixels inside the processing window are 0 or 255 then pixel value is retained considering it as texture. Step 5: Steps 1 to 4 is repeated until all pixels of the entire image is processed Vasanth, K., S. Karthik, and V. Rajesh. "A decision based unsymmetrical trimmed variants for the removal of high density salt and pepper noise." International Journal of Computer Applications 42.15 (2012): 53-66.