A Perceptually Adaptive Approach to Image Denoising

Over the years a variety of methods have been introduced to remove noise from digital images, such as Gaussian filtering, anisotropic filtering, and Total Variation minimization. Due to certain assumptions made about the frequency content of the image, many of these algorithms remove fine details from images in addition to the noise. The non-local means algorithm assumes the concept of self-similarity, instead of making the above mentioned assumptions. This concept of self-similarity is used in the NLM algorithm to perform image denoising. However, many of these algorithms remove the fine details and structure of the image in addition to the noise because of assumptions made about the frequency content of the image. The non-local means algorithm does not make these assumptions, but instead assumes that the image contains an extensive amount of redundancy. These redundancies can then be exploited to remove the noise in the image. This paper will implement the approach and compare it to other denoising methods using the method noise measurement. Recently, the NLMeans filter has been proposed by Buades et al. for the suppression of white Gaussian noise. This filter exploits the repetitive character of structures in an image, unlike conventional denoising algorithms, which typically operate in a local neighborhood. In this paper, we show that the NLMeans algorithm is basically the first iteration of the Jacobi optimization algorithm for robustly estimating the noise-free image.