A NEW APPROACH FOR UNSUPERVISED RESTORING IMAGES BASED ON WAVELET-DOMAIN PROJECTION PURSUIT LEARNING NETWORK

The Wavelet-Domain Projection Pursuit Learning Network (WDPPLN) is proposedfor restoring degraded image. The new network combines the advantages of both projectionpursuit and wavelet shrinkage. Restoring image is very difficult when little is known about apriori knowledge for multisource degraded factors. WDPPLN successfully resolves this problemby separately processing wavelet coefficients and scale coefficients. Parameters in WDPPLN,which are used to simulate degraded factors, are estimated via WDPPLN training, using scalecoefficients. Also, WDPPLN uses soft-threshold of wavelet shrinkage technique to suppress noisein three high frequency subbands. The new method is compared with the traditional methodsand the Projection Pursuit Learning Network (PPLN) method. Experimental results demonstratethat it is an effective method for unsupervised restoring degraded image.

基于投影寻踪子波学习网络的图像无监督恢复

针对现有模糊图像的复原方法 ,提出了一类新型人工神经网络——投影寻踪子波学习网络 ,并将其用来处理图像的去模糊问题。这类新型网络具有投影寻踪学习网络优点 ,在先验条件知道甚少的情况下 ,不用求点扩展函数 ,直接通过网络的学习 ,提取参数 ,以达到自适应剔除图像的模糊信息 ,恢复原图像 ;且具有小波函数的时域局部性 ,可以对多种噪声源的模糊图像进行恢复。模拟结果表明 。