A Study On Infrared Image Processing Based On Wavelet Transform

In recent years, infrared images are applied widely to many domains. However,much noise and blurred edge is a serious problem to the infrared images because of the inherent character of infrared detectors. In order to reduce these phenomena and improve the image quality the technique of wavelet analysis is introduced.The wavelet transform decomposes an image into a finite number of resolution scales that is very suitable for image analysis. And after the wavelet reconstruction, the visual quality of the processed images can be improved effectively.The research is mainly focused on the Infrared image de-noising and edge detection based wavelet transform.The new results are as follows: introduced some key problems of wavelet transform, proposed a scheme of infrared image de-noising based wavelet threshold processing.Aimed at the characteristic of noise distribution, the multiplicative noise in infrared image is turned into additive noise using a logarithm. By thresholding the wavelet coefficients of noisy infrared image, the de-noised image can be reconstructed. By the analysis of wavelet multi-scale edge detection principle, and different regulation of wavelet transformation to the noise and edge, we developed traditional-based improvement edge detection algorithm.The simulation results show that the image denoising effect of our scheme has obvious superiority compared with general wavelet transform method. And our improvement edge detection algorithm can increase the edge clearness and continuity. In a word, such schemes can achieve a considerable improvement in noise smoothing, details-preserving and the visual quality of infrared images.