Speckle noise reduction and image segmentation based on a modified mean filter

Abstract

Image segmentation is an essential process in many fields involving digital images. In general, segmentation is the process of dividing the image into objects and background image. Image segmentation is an important step in the object detection process. It becomes more critical if a given image is corrupted by noise. Most digital images are corrupted by noises such as salt and pepper noise, Gaussian noise, Poisson noise, speckle noise, etc. Speckle noise is a multiplicative noise that affects pixels in a gray-scale image, and mainly occurs in low level luminance images such as Synthetic Aperture Radar (SAR) images and Magnetic Resonance Image (MRI) images. Image enhancement is an essential task to reduce specklenoise prior to performing further image processing such as object detection, image segmentation, edge detection, etc. Here, we propose a neighborhood-based algorithm to reduce speckle noise in gray-scale images. The main aim of the noise reduction technique is to segment the noisy image. So that the proposed algorithm applies some luminance to the original image. The proposed technique performs well at maximum noise variance. Finally, the segmentation process is done by the modified mean filter. The proposed technique has three phases. In phase 1, the speckle noise is reduced and the contrast adjustment is made. In phase 2, the segmentation of the enhanced image is processed. Finally, in phase 3, the isolated pixels in the segmented image are eliminated and the final segmented image is generated. This technique does not require any threshold value to segment the image; it will be automatically calculated based on the mean value.