Comparative Study of Non-local Means and Fast Non –local Means Algorithm for Image Denoising

Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age. All digital images contain some degree of noise. Removing noise from the original signal is still a challenging problem for researchers. In this paper, the non-local denoising approach presented by Buades et al. is compared and analyzed by Fast nonlocal means algorithm. The original non-local means method is based on Self Similarity concept. Non local means denoising algorithm has disadvantage that remarkable denoising results are obtained at high expense of computational cost due to the enormous amount of weight computations. In order to accelerate the algorithm a new one that reduces the computation cost for calculating the similarity of neighborhood window was developed, known as Fast nonlocal means algorithm. In this algorithm , an approximate measure about the similarity of neighborhood windows, an efficient Summed Square Image (SSI) scheme and Fast Fourier transform (FFT) were used to accelerate the calculation of this measure. Furthermore, results obtained by simulation using Matlab7.0 shows that Fast nonlocal means algorithm is fifty times faster than original non –local means algorithm .We finally demonstrate the potential of the both algorithms through comparisons. We also present denoising results obtained on real images.