The guided filter is derived from a local linear model. It computes the filtering output by considering the content of a guidance image (self-image or reference image). The guided filter can be used as an edge-preserving smoothing operator that behaves better near edges. It can transfer the structures of the guidance image to the filtering output, enabling new filtering applications like dehazing and guided feathering. Moreover, the guided filter naturally has a fast and non-approximate linear time algorithm, regardless of the kernel size and the intensity range. Regularization parameter (ϵ) is one of the two important factors which determines the characteristics of the guided image applied to the input image i.e. it helps to perceive the degree of the edges of an image. In this paper, we will observe the effect of the regularization parameter on a guided filtered image.