Abstract
Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.
1 Introduction
Many scientific data sets are contaminated by noise because of data acquisition process and/or transmission, which can degrade the signal of interest. A first pre-processing step in analyzing such data sets is denoising, that is, estimating the signal of interest from the available noisy data [1].
Even though denoising has long been a focus of research, there always remains room for improvement, especially in image denoising. For images, noise suppression/reduction is a delicate and a difficult task because there is a tradeoff between noise reduction and preservation of actual image features. If high-frequency noise is to be removed from the corrupted image, the simple spatial filtering may be sufficient, but at the cost of computational complexity involved in performing the convolution. This can be reduced by Frequency-domain methods where convolution is transformed into multiplication of the spectra due to Fourier convolution property. As the noise is spread across all frequencies, the frequency-based denoising methods adopt low-pass filtering to suppress most of high-frequency components in order to denoise the image. However, this is generally not effective as it suppresses both noise and other high-frequency features of the image resulting in an overly smoothed denoised image.
5 Conclusions
In this paper, the amalgamation of NL means filter and its method noise thresholding using wavelet has been proposed. The performance of the proposed methods is compared with WT-based approach, BF, MRBF and NL means filter. Through experiments conducted on standard images, it was found that the proposed method has improved the results of WT approach, BF, NL means filter and MRBF with slight increase in performance in terms of method noise, visual quality, PSNR and IQI. Only in few cases MRBF has shown improved performance when compared to the proposed method.
