基于数学形态学和小波阈值的红外温度图像去噪方法

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针对普通小波阈值去噪方法不能较好地保留红外图像边缘信息的问题，提出一种数学形态学边缘检测和小波阈值去噪相结合的方法，即红外图像先经过小波变换，在高频子带中做数学形态学边缘检测，确定边缘信息的位置，再进行阈值去噪处理。结果表明，与普通小波阈值去噪方法相比，该方法较好地保留了红外图像的边缘信息，去噪效果明显，且改善了均方误差和峰值信噪比，对摩擦副表面红外温度图像进行去噪，可获得较为准确的温度场。 The common method of wavelet threshold denoising is not able to retain the edge information in infrared images better. A method combining the mathematical morphology edge detection and the wavelet threshold denoising was presented and applied in denoising the infrared image. Infrared image was processed through three steps：wavelet transformation ,mathematical morphology edge detection in the high-frequency sub-band for determining the location of edge and threshold denoising. The results show that this method can preserve more edge information,improve mean square deviation and peak signa/-to-noise ratio, and obtain the accurate temperature field in denoising the infrared image of friction surface.