鉴于2维张量积小波已被证实不能为分片光滑图像提供理想表达，从而促使了近年来各种“超越小波”的变换理论和方法的出现。其中轮廓波变换因其理论新颖、技术思想先进、实效显著而具有深入研究的前景和潜力。为了使人们对轮廓波变换有一概略了解，首先以轮廓波变换原理及变换域隐马尔可夫模型为主线，并对关键点给出了详细论述；然后从宏观角度对轮廓波变换进行了深入浅出的综述；最后通过与小波变换的应用对比、分析，指出了轮廓波的应用潜力之所在，以及更进一步的研究方向。

It has become evident that the commonly used two-dimensional tensor product wavelet bases are not optimal for representing signals that resemble images. This motivated a variety of schemes beyond wavelets, such as ridgelets, curvelets, contourlets, and wedgelets. Contourlet transform provides an efficient representation for two-dimensional piecewise smooth images, and constructed in discrete domain. In this paper, contourlets and hidden Markov model using contourlet transform are discussed, and the deep insights on the keys to understanding the contourlet transform are stated in details. The applications of contourlets, comparing with wavelets, are also introduced, and comment on the potential of contourlets for these applications in the future is given.