Detection and Removal of Salt and Pepper Noise by Gaussian Membership Function and Guided Filter

The performance of vision-based algorithms depends on the quality of digital images. The images corrupt with salt and pepper noise during image acquisition and transmission deteriorate the performance of these algorithms. This generates the necessity of enhancement algorithms for noise removal. This paper presents an approach for salt and pepper noise removal from digital images. Firstly, the corrupt pixels are detected using a Gaussian membership function and then denoising of these corrupt pixels is performed by a combination of Gaussian and Guided filter. A digital image contains visual information in the form of pixel intensities and regions with abrupt intensity changes. The image regions with similar pixel intensities are called homogeneous and the regions with abrupt intensity changes are called edges or textures. These regions are responsible for carrying important image details. The objective of image denoising is to retrieve the actual pixel intensities in such regions. The proposed approach aims to identify and denoise the corrupt pixels by salt and pepper noise such that the details present in homogeneous regions and edges remain unchanged. For detection of corrupt pixels, two thresholds are estimated using Gaussian membership function. Then, a combination of Gaussian and Guided filter is used for denoising these detected corrupt pixels. The Gaussian filter helps to assign proper weights to the neighborhood pixel set for averaging. However, the Guided filter helps to maintain the structure of an image at very high noise level. The experiments are performed on standard images used in literature with different noise levels up to 99%. It shows that the proposed approach performs efficiently in terms of peak signal to noise ratio.