In the image processing, noise is referred to as the visual distortion. This undesirable by-product may be captured in
an image due to unpreventable assorted reasons. The interference
of natural phenomena and technical problem, such as small sensor
size, long exposure time, low ISO, shadow noise etc., can pollute
image. The presence of noise images affects image processing outputs that include segmentation. Segmentation for noisy images is
the major concern. To tackle this issue, we propose a modernistic
model that is able neutralize the negative effects of outlier using
the characteristic of kernel function by different approaches such
as linear approach and quadratic approach for global segmentation. Moreover the weight function is used for local segmentation
of noisy images. Comparing with classical models, the proposed
technique shows robust performance. In comparison with the wellknown models such as Chan-Vese (CV) model , Yongfei Wu and
Chuanjiang He (Wu-He) model and Chunming Li (Li) model we
conclude that performance of our new model is much better.