This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 1 Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition Hasan Demirel, Cagri Ozcinar, and Gholamreza Anbarjafari Abstract—In this letter, a new satellite image contrast enhance- ment technique based on the discrete wavelet transform (DWT) and singular value decomposition has been proposed. The tech- nique decomposes the input image into the four frequency sub- bands by using DWT and estimates the singular value matrix of the low–low subband image, and, then, it reconstructs the en- hanced image by applying inverse DWT. The technique is com- pared with conventional image equalization techniques such as standard general histogram equalization and local histogram equalization, as well as state-of-the-art techniques such as bright- ness preserving dynamic histogram equalization and singular value equalization. The experimental results show the superiority of the proposed method over conventional and state-of-the-art techniques. Index Terms—Discrete wavelet transform, image equalization, satellite image contrast enhancement. I. I NTRODUCTION S ATELLITE images are used in many applications such as geosciences studies, astronomy, and geographical infor- mation systems. One of the most important quality factors in satellite images comes from its contrast. Contrast enhancement is frequently referred to as one of the most important issues in image processing. Contrast is created by the difference in luminance reflected from two adjacent surfaces. In visual perception, contrast is determined by the difference in the color and brightness of an object with other objects. Our visual system is more sensitive to contrast than absolute luminance; therefore, we can perceive the world similarly regardless of the considerable changes in illumination conditions. If the contrast of an image is highly concentrated on a specific range, the information may be lost in those areas which are excessively and uniformly concentrated. The problem is to optimize the contrast of an image in order to represent all Manuscript received July 27, 2009; revised September 18, 2009. H. Demirel is with the Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazima˘ gusa, via Mersin 10, Turkey (e-mail: hasan.demirel@emu.edu.tr). C. Ozcinar is with the Department of Electronic Engineering, University of Surrey, GU2 7XH Surrey, U.K. and also with the Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimaðusa, via Mersin 10, Turkey (e-mail: co00048@surrey.ac.uk). G. Anbarjafari is with the Department of Information System Engineering, Cyprus International University, Lefko¸ sa, via Mersin 10, Turkey (e-mail: sjafari@ciu.edu.tr). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/LGRS.2009.2034873 the information in the input image. There have been several techniques to overcome this issue [1]–[4], such as general histogram equalization (GHE) and local histogram equalization (LHE). In this letter, we are comparing our results with two state-of-the-art techniques, namely, brightness preserving dy- namic histogram equalization (BPDHE) [5] and our previously introduced singular value equalization (SVE) [6]. In many image processing applications, the GHE technique is one of the simplest and most effective primitives for con- trast enhancement [7], which attempts to produce an output histogram that is uniform [8]. One of the disadvantages of GHE is that the information laid on the histogram or probability dis- tribution function (PDF) of the image will be lost. Demirel and Anbarjafari [9] showed that the PDF of face images can be used for face recognition; hence, preserving the shape of the PDF of an image is of vital importance. Techniques such as BPDHE or SVE are preserving the general pattern of the PDF of an image. BPDHE is obtained from dynamic histogram specification [10] which generates the specified histogram dynamically from the input image. The singular-value-based image equalization (SVE) tech- nique [6], [9] is based on equalizing the singular value matrix obtained by singular value decomposition (SVD). SVD of an image, which can be interpreted as a matrix, is written as follows: A = U A Σ A V T A (1) where U A and V A are orthogonal square matrices known as hanger and aligner, respectively, and the Σ A matrix contains the sorted singular values on its main diagonal. The idea of using SVD for image equalization comes from this fact that Σ A contains the intensity information of a given image [11]. In our earlier work [6], [9], SVD was used to deal with an illumination problem. The method uses the ratio of the largest singular value of the generated normalized matrix, with mean zero and variance of one, over a normalized image which can be calculated according to ξ = max ( Σ N(μ=0,var=1) ) max(Σ A ) (2) where Σ N(μ=0,var=1) is the singular value matrix of the syn- thetic intensity matrix. This coefficient can be used to regener- ate an equalized image using Ξ equalized A = U A (ξ Σ A )V T A (3) 1545-598X/$26.00 © 2009 IEEE Authorized licensed use limited to: ULAKBIM UASL - DOGU AKDENIZ UNIV. Downloaded on December 4, 2009 at 02:42 from IEEE Xplore. Restrictions apply.