Image enhancement method based on wavelet transform

Bi Xu; Chengyu Fu; Jiaguang Ma

doi:10.1117/12.391926

17 July 2000 Image enhancement method based on wavelet transform

Bi Xu, Chengyu Fu, Jiaguang Ma

Proceedings Volume 4044, Hybrid Image and Signal Processing VII; (2000) https://doi.org/10.1117/12.391926
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

The wavelet transform theory emerged in recent years has been applied in many fields, such as image compression, edge and feature detection, image enhancement and texture analysis. We have studied some of the developments that lead to the current state of image enhancement, sufficiently considering the configurations of video image and eyespot requirement. This paper presents a new method to accentuate image using multi-scale analysis, time-frequency wavelet transform and minimum distance criteria. After decomposing an image into components of different size, position, and orientation, the amplitude of coefficients in the wavelet transform domain can be altered prior to obtaining the inverse transform. This can selectively accentuate interesting components at the expense of undesirable ones. Experimental results have shown that simpler algorithm, faster running speed, rather improved SNR and contrast can be obtained with this method, resulting in a good enhancement effect.

Citation Download Citation

Bi Xu, Chengyu Fu, and Jiaguang Ma "Image enhancement method based on wavelet transform", Proc. SPIE 4044, Hybrid Image and Signal Processing VII, (17 July 2000); https://doi.org/10.1117/12.391926

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available