tailieunhanh - Báo cáo hóa học: " Piecewise Linear Model-Based Image Enhancement Fabrizio Russo"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Piecewise Linear Model-Based Image Enhancement Fabrizio Russo | EURASIP Journal on Applied Signal Processing 2004 12 1861-1869 2004 Hindawi Publishing Corporation Piecewise Linear Model-Based Image Enhancement Fabrizio Russo Department of Electrical Electronic and Computer Engineering DEEI University of Trieste Via Valerio 10 Trieste 34127 Italy Email rusfab@ Received 1 September 2003 Revised 23 March 2004 A novel technique for the sharpening of noisy images is presented. The proposed enhancement system adopts a simple piecewise linear PWL function in order to sharpen the image edges and to reduce the noise. Such effects can easily be controlled by varying two parameters only. The noise sensitivity of the operator is further decreased by means of an additional filtering step which resorts to a nonlinear model too. Results of computer simulations show that the proposed sharpening system is simple and effective. The application of the method to contrast enhancement of color images is also discussed. Keywords and phrases image enhancement sharpening noise reduction nonlinear filters. 1. INTRODUCTION It is known that a critical issue in the enhancement of images is the noise increase that is typically produced by the sharpening process 1 . A classical example is represented by the linear unsharp masking UM method. Since a fraction of the high-pass filtered image is added to the original data the resulting effect produces edge enhancement and noise amplification as well. In order to address this issue more effective approaches resort to nonlinear filtering that can realize a better compromise between image sharpening and noise attenuation 2 3 4 5 6 . In particular weighted medians WMs have been successfully experimented as a replacement for high-pass linear filters in the UM scheme 7 . In this framework methods based on permutation weighted medians PWMs offer very interesting results because they can prevent the noise amplification during the enhancement process 8 9 . Polynomial UM approaches constitute another family