【正文】 tical pictures processing. Recently, the wavelet transformation has emerged as an excellent methodology for small targets detection because it gives a lot of advantages among all other imageprocessing techniques. Simply speaking, in small and dim target detection application, the wavelet transformation can serve as a matching filter, a multiresolution image analyzer, a multidimensional image analyzer, a singularity detector, and an orthogonal extractor [3][4]. A method based on wavelet transformation, which is to detect small and dim target by background suppression, contains four steps [5]: firstly, extracts approximate coefficients of original image, which include some features of original image。 thirdly, uses the background image subtracted from the original image, and then obtains an image which is called the background suppressed image, mainly includes target and noise point information。 2) the number of deposition levels. In order to overe these difficulties and improve the testing results, a novel method to detect small targets under plex seasky background is proposed in the paper. It is based on the empirical mode deposition (EMD). Compared with wavelet transformation, EMD algorithm shows a superior performance on selectivity and the precision of data analysis. It is a powerful tool for adaptive multiscale analysis of short time nonlinear and nonstationary signals. EMD method is first proposed by Norden E. Huang [6] in 1998. The method can extract a series of intrinsic mode functions (IMF) by deposition the local energy associated with the intrinsic time scales of the signal itself. So it is selfadaptive and can depict the timefrequency characteristics of the signal exactly. The outline of the paper is as follows: Section 2 describes the general procedures of small targets detection. Section 3, a novel small infrared target detecting algorithm based on EMD algorithm is presented. Section 4 presents some experimental results and pares the testing results based on EMD method with wavelet transformation in some ways. And Section 5 draws some conclusions. 2 Common procedures of small target detection Target detection algorithms have been steadily improving, whereas many of them failed to work robustly during applications involving changing backgrounds that are frequently encountered. In general, a small target embedded in cloudy background presents as a gray spot in image, which also contains bright illuminated terrain or sunlit clouds. That is to say, when an infrared sensor is far away from the targets, the targets immerged in heavy noise and clutter background present as spotlike feature which have the signature of discontinuity paring with its neighbor region, no obvious structural information in infrared image. The gray value of a target is higher than its immediate background in infrared image and is not partially correlative with its local neighborhood. Due to pixel nonuniformity of response of infrared image, the atmospheric transmitting and scattering, the plex background containing largearea of cloud and ocean waves and so on, the background in infrared scene shows spatial correlation between each pixel and its surroundings and being undulant significantly, which in frequency domain lies in low frequency band, belonging to low frequency interfered for target detection [7][8]. Furthermore, it is important to note that noise e from the infrared sensor and the background. Because of the effects of inherent sensor noise, the natural factors such as weather, wind, sun light and so on, there exist some high gray regions in the infrared image as plicated cloud edge, irregular sun light spot, etc., both of which and targets can be considered as homogeneous region and fall in high frequency band of frequency spectrum, belonging to high frequency interfered for target detection. So a spatial frequency filter is designed to suppress the background of an infrared image, and then detect of target through a threshold is very efficient. EMD algorithm deposes a signal into a series of different frequency ponents, which is similar to wavelet transformation. The reason why the algorithm has an advantage over wavelet transformation is that the deposition based on EMD algorithm is selfadaptive. Therewithal, in detected application of small target, the result based on EMD algorithm is equal to wavelet transformation, and even more proponent in some aspects in theory. Some detected results of small target based on EMD algorithm and wavelet transformation are given in Section 4. Some parisons of the two methods are also given in the same chapter. Image signal modeling An infrared image contains small targets can be described as follows: a r g( , y ) ( , ) ( x, y ) n( x, y )t e t ba c kf x f x y f? ? ? (21) where ftarget(x, y), n(x, y), fback(x, y) denote the target signal, the noise signal and clutter background respectively. In this image signal modeling, n(x, y) is generally assumed that it is Gaussian white noise with zero mean. For a single image mentioned above, we don?t know the power of the signal and the noise, so the signal to noise ratio (SNR) of the image may be estimated approximately, which is now still at issue. A simple SNR is defined as the ratio of the variance of target and the background image. Definition 1: If an image signal modeling as before depicts, the SNR may be defined as： /SNT s?? (22) where s and σ denote the variance of the target and the background image respectively. Background suppression technique Since small targets and clutter backgrounds have different spatial frequency characteristics, a spatial frequency filter could be designed to suppress clutter backgrounds, which provides a possibility to detect small targets. So some adaptive an