Image noise reduction based on adaptive thresholding and clustering

In this paper, we present a novel image denoising method based on adaptive thresholding and k-means clustering. In this method, we adopt the adaptive thresholding technique as an alternative to the traditional hard-thresholding of the block-matching and 3D filtering (BM3D) method. This technique has a high capacity to adapt and change according to the amount of the noise. More precisely, in our method the soft-thresholding is applied to the areas with heavy noise, on the contrary the hard-thresholding is applied to the areas with slight noise. Based on the adaptation and stability of the adaptive thresholding, we can achieve optimal noise reduction and maintain the high spatial frequency detail (e.g. sharp edges). Owing to the capacity of k-means clustering in terms of finding the relevant candidate-blocks, we adopt this clustering at the last estimate to partition the denoised image into several regions and identify the boundaries between these regions. Applying k-means clustering will allow us to force the block matching to search within the region of the reference block, which in turn will lead to minimize the risk of finding poor matching. The main reason of applying the K-means clustering method on the denoised image and not on the noised image is specifically due to the flaw of accuracy in detecting edges in the noisy image. Experimental results demonstrate that the new algorithm consistently outperforms other reference methods in terms of visual quality, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, in the proposed algorithm the time consumption of the image denoising is less than that in the other reference algorithms.