An Efficient Image Denoising Scheme for Higher Noise Levels Using Spatial Domain Filters

Image denoising is one of the fundamental image processing problem. Images are corrupted with additive white Gaussian noise during image acquisition and transmission over analog circuits. In medical images the prevalence of noise can be perceived as tumours or artefacts and can lead to first diagnosis. Similarly in satellite images the visibility of images is significantly degraded due to noise, hence the image denoising is of vital importance. There are many denoising mechanisms given in literature are able to work well on lower noise levels but their performance degrades with increasing noise levels. If higher amount of filtering is applied it leads to degradation or removal of edges from the image and hence significant information. In this paper, we proposed an algorithm in which we are able to address the problem of image denoising at higher noise levels while preserving the edge information. The standard bilateral filter does not provides good results at higher noise levels. Hence we proposed to combine robust bilateral filtering with anisotropic diffusion filtering as the anisotropic diffusion perform the smoothing of homogenous regions without blurring the edges. Experimental results show that the proposed method works better for higher Nosie levels in terms of PSNR values and Visual quality.