Weighted median image sharpeners for the World Wide Web

Abstract

A class of robust weighted median (WM) sharpening algorithms is developed in this paper. Unlike traditional linear sharpening methods, weighted median sharpeners are shown to be less sensitive to background random noise or to image artifacts introduced by JPEG and other compression algorithms. These concepts are extended to include data dependent weights under the framework of permutation weighted medians leading to tunable sharpeners that, in essence, are insensitive to noise and compression artifacts. Permutation WM sharpeners are subsequently generalized to smoother/sharpener structures that can sharpen edges and image details while simultaneously filter out background random noise. A statistical analysis of the various algorithms is presented, theoretically validating the characteristics of the proposed sharpening structures. A number of experiments are shown for the sharpening of JPEG compressed images and sharpening of images with background film-grain noise. These algorithms can prove useful in the enhancement of compressed or noisy images posted on the World Wide Web (WWW) as well as in other applications where the underlying images are unavoidably acquired with noise.

Particle swarm based unsharp masking

“…In this section the visual quality of the enhanced edges used in this paper are described in detail, followed by quantitative measures such as Focus, Blur metric, Edge sharpness. The effectiveness of the proposed work is illustrated by comparing it with the existing methods which are Weighted Median Filter (WMF) [9], Quadratic Volterra filter (QVF) [6], Quadratic Weighted Median Filter (QWM) [7] and Edge-Detected Guided Morphological Filter (ED-MOG) [8]. …”

“…An order statistical filter, lower-upper-middle (LUM) filter is proposed to smoothen, sharpen and outlier rejection [5]. Weighted median filter (WMF) has been experimented as a replacement for high-pass filters in the UM and also provides outlier suppression [6]. The extension of linear combination of polynomial terms in quadratic volterra(QV) filters [7] with WM, called quadratic weighted median filter (QWM) is derived to yield robust outlier rejection and noise suppression [8].…”

A new image sharpening approach for single‐sensor digital cameras

“…In off-camera sharpening, such as for web and monitor viewing and print reproduction, the edge enhancement process is often manually controlled by tuning the parameters in various unsharp masking and orderstatistic solutions (Polesel, et al, 2000;Fischer et al, 2002) to avoid both the introduction of sharpening halos and the increase in the visibility of jaggedness, noise, and various demosaicking artifacts. However, in-camera sharpening requires different solutions due to the real-time constraints and the need for fully automated processing.…”

“…To compensate for the image blurring, digital cameras enhance the visual quality of the demosaicked output using image sharpening techniques (Parulski and Spaulding, 2002;Ramanath et al, 2005). Image sharpening utilizes high-pass type operations to enhance the high-frequency content of the image, such as edges and fine details (Polesel, et al, 2000;Fischer et al, 2002). Since edge information is very important for the human perception (Bouzit and MacDonald, 2000), its preservation and possibly enhancement is of paramount importance for any imaging system (Koschan and Abidi, 2005;.…”

A Prototype of Autonomous Intelligent Surveillance Cameras

“…Methods of sharpness enhancement found in literature [7][8] are in the context of compression algorithms (e.g. JPEG), whereas a few deal directly on the luminance (Y) and chrominance (U/V) signals [9].…”