A three stage integrated denoising approach for grey scale images

Restoration of the images contaminated with Additive White Gaussian Noise (AWGN) is a fundamental operation at the interface of statistical and functional analysis. It is an extremely exigent job to design a noise suppression algorithm with edge and feature detail preservation owing to random manifestation of the noise amongst the pixels. In spite of documentation of the sophisticated denoising algorithms in literature, a desired amount of applicability is not achieved as they leave residual noise, create artifacts and remove fine structures. In this paper we propose an integrated image denoising algorithm which exploits the bit plane slicing based image decomposition approach for residual noise removal especially at higher noise levels. The intuitive idea of segregation of noise in decomposed bit planes and selective criterion of denoising lower order bit planes with adaptive bitonic filtering is able to preserve feature details while showing strong denoising performance. Experimental analysis shows that the proposed methodology gives considerable results at low noise values and outperforms other existing techniques at higher noise values both in terms of qualitative and quantitative performance.