International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Sharpness Enhancement and Denoising of Image Using L1-Norm Minimization Technique in Adaptive Bilateral Filter

Shahla Naureen Khan, Chhabikiran Sao

Abstract: In Image processing, Image restoration technique plays a vital role. Removing mixed noise from images is a challenging problem. This paper presents a novel technique of Image sharpening and Denoising using the L1-norm minimization technique in Adaptive Bilateral Filtering. The objective of this paper is to explore the advantage of new technique over the existing ones. This employs comparison of results obtained using both L1 and L2-norm minimization techniques. In this paper, we consider Least Absolute Deviation (L1) method for image restoration in ABF. This method finds application in many areas due to its robustness as compared to Least Squares method (L2).

Keywords: L1 and L2 norms, LAD, Least squares, ABF, Image Restoration

How to Cite?: Shahla Naureen Khan, Chhabikiran Sao, "Sharpness Enhancement and Denoising of Image Using L1-Norm Minimization Technique in Adaptive Bilateral Filter", Volume 3 Issue 11, November 2014, International Journal of Science and Research (IJSR), Pages: 1649-1652, https://www.ijsr.net/getabstract.php?paperid=OCT141352, DOI: https://dx.doi.org/10.21275/OCT141352

Download Citation: APA | MLA | BibTeX | EndNote | RefMan

Share This Research

Help this article reach readers, researchers and professionals.

Share activity is measured for research-engagement analytics. Only verified, unique public shares can support award tie-breaking.

Confirm Your Share

Enter your details so IJSR can confirm this sharing activity.

Your details are used to validate this share and protect the award process from duplicate or false activity.

Download Article PDF


Rate This Article!

Top

Confirm Your Share

Enter your details so IJSR can confirm this sharing activity.

Your details are used to validate this share and protect the award process from duplicate or false activity.