The Application Of Empirical Mode Decomposition In Image Processing

Generally speaking, the image processing means the digital image processing. Digital image is a big2demensional array, which is acquired through sampling and quantization, using digital camera, scanner ect.The element of the array is called pixel with a value of integer.The value is also called grayscale value.The techniques of image processing mainly contain image compression, image enhancement and restoration, image matching, describing and recognition. The common processing methods include sampling and quantization, image coding, image enhancement, image restoration, imgage segmentation and image analyzing and so on. In fact,digital image processing has been widely used in many different fields such as Astronomy,Remote Sensing,products checking,safety checking.The empirical mode decomposition technique is a breakthough in signal analyzing.It can express the original signal in the form of intrinstic mode functions, which reflect its physical charactaristic. Since the empirical mode decomposition was proposed by Norden E.Huang in1998,it becomes one of the most effective method to analysing the nonlinear and nonstable signal.In recent years, researchers have extended this method to2dimentions.Since the digital image in computer are in the form of matrix,the bi-dimension empirical mode decomposition can be used in image processing.For the empirical mode decompositon, singals are decomposed to a series of intrinstic mode functions from high frequency to low frequency and a residual with one extremal point at most. Bidimentional empirical mode decomposition can get the detail information and tendency information from a given picture. Different layouts of the image have different resolutions,we can modify the detail information in different layouts to enhance the original image by strenthening the detail information.At the same time, the high frequency part exists in detail information and noise also exist in the high frequency part.So noise can be removed partly.The tendency part represents the whole tendency of the image,this part also represents the whole lightness of the image,which can be used to lighten the images that are dark to some degree. The spatial similarity property makes it possible to estimate the bidimentianal empirical decomposition at both and low resolution levels, thus resulting in compression. It can also be used in image compression and deression, which avoid selecting the base in discrete wavelet transform.In this paper, some studies have been done based on the conclusions in recent years, which contain:The first chapter, the background of this paper and the aim and meaning of this paper are introduced. The current situation of this field has also been briefly described and the main work of this paper.The second chaper, the empirical mode decomposition (EMD) and bi-dimensional mode decomposition method have been discused in detail.The third chapter, the basic concepts and principals are given.we also discuss some typical methods in digital image processing.The forth chapter introduce the improved empirical mode decompostion based on the current conclusions on EMD,and apply it in two aspect:image enhancement and image denoising.In the fifth chapter, we summerise the whole things which are talked above, and ponit out some problems in this paper and tell the remaining work that should be done in the following research.