This paper is published in Volume-6, Issue-3, 2020
Area
Image Processing & Computer Vision
Author
Eisita Basak, Sangita Roy, Dr. Saradindu Panda
Org/Univ
Narula Institute of Technology, Kolkata, West Bengal, India
Pub. Date
19 June, 2020
Paper ID
V6I3-1539
Publisher
Keywords
Guided Filter, Regularization Parameter (ϵ), Local Linear Model, Edge-Preserving Filtering

Citationsacebook

IEEE
Eisita Basak, Sangita Roy, Dr. Saradindu Panda. Effect of the regularization parameter on image-guided filter, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Eisita Basak, Sangita Roy, Dr. Saradindu Panda (2020). Effect of the regularization parameter on image-guided filter. International Journal of Advance Research, Ideas and Innovations in Technology, 6(3) www.IJARIIT.com.

MLA
Eisita Basak, Sangita Roy, Dr. Saradindu Panda. "Effect of the regularization parameter on image-guided filter." International Journal of Advance Research, Ideas and Innovations in Technology 6.3 (2020). www.IJARIIT.com.

Abstract

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.