Impulse Denoising Algorithm for Gray and RGB Images

Abstract

Noise removal plays vital role in image processing and also important pre processing task before performing post operation like Image segmentation etc.. This paper presents a effective and efficient algorithm in order to remove impulse noise from gray scale and color images. Challenging results show the superior performance of the proposed filtering algorithm compared to the other standard algorithms such as Standard Median Filter (SMF), Median Filter (MF), Weighted Median Filter (WMF) and Trimmed Median Filter (TMF). Furthermore, various performance metrics such as the MSE, PSNR and SSIM have been compared with Existing standard algorithms. The computational time for the denoised image is calculated for different noise levels and the proposed algorithm has lower computational time, hardware complexity and ease in operation.The obtained results prove that it has better qualitative analysis by improving visual appearance and challenging quantitative measures even at high noise densities ranging up to 90%. General Terms Impulse noise removal technique KeywordsImpulse noise, Median filter, Peak signal to noise ratio, Mean square error, Salt and pepper noise, Structural similarity index metric.

A novel adaptive tolerance filter for random valued impulse noise suppression in digital grey and color images

“…From the results it is evident that the proposed filter out performs all other filtering schemes that use fixed and empirically obtained threshold values and fixed size windows. However the simulation results of UTMF and Ref (12) filters are somewhat encouraging as compared to SMF, WMF and TMF. From the comparative analysis we can conclude that the filters designed for salt and pepper impulse noise suppression, exhibit poor performance in suppressing the random valued impulse noise.…”

“…As said earlier, the effective use of this method requires optimum values for parameters such as maximum window size 'W max ', and the dynamic threshold values T d .To maximize the probabilities that only wanted pixels take part in filtering operation and to reduce the computational time, above said parameters must be chosen of appropriate values. Table 2 presents the Peak Signal to Noise Ratio (PSNR) and Root Mean Square Error (RMSE) results comparison of proposed novel Adaptive Tolerance Filter (ATF) with various other filters namely SMF, WMF, UTMF and TMF and Ref (12) obtained for the noisy 'Lena grey' image contaminated with random valued salt and pepper noise of densities ranging from 10% to 90%. From the results it is evident that the proposed filter out performs all other filtering schemes that use fixed and empirically obtained threshold values and fixed size windows.…”

“…In that case the centre pixel is replaced with the processed immediate neighborhood pixel [19], hence edge could be preserved which in turn reduces computational complexity. The benefits such as reduction in computational complexity, hardware complexity and increase in speed of operation etc are possible to achieve using several methods of sorting [11], [12], and [20]. It has been suggested by the Rajamani et al, and is known as Lone Diagonal Sorting (LDS) [12] algorithm in which three right diagonal pixels were considered for sorting and median value of three elements is computed instead of nine elements sorting as in conventional approach.…”

A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images

“…Furthermore, this method has reasonable computation time in high noise densities (CT).Leading Diagonal Sorting Algorithm [19] is used with the fixed 3x3 size working window to compute the median. Truncated mean is computed by defining the boundaries and truncating the pixel values in the filtering window that fall outside the defined boundary.…”

“…For real time applications, computationally efficient algorithms are needed. The work presented in this paper uses Leading Diagonal Sorting Algorithm (LDSA) proposed and used by Rajmani and Keshveni [19] in which the sorting of the leading diagonal elements alone is done rather than traditional row, column and diagonal sorting as a whole. In this method only 3 pixels are involved for sorting and both the computational and hardware complexity are reduced to a larger extent.…”