Rehabilitation of Ultra-Resolution Images Based on Improving Machine Learning
Compared with the low-resolution image,the high-resolution image needs more pixels,as well as high-frequency information.When the contrast between the target and the background is high,it is difficult to restore the high-frequency details of the image.Therefore,a method of restoring super-resolution image details based on im-proved machine learning was proposed.Firstly,the noise was removed from images,and then the contrast of the image was enhanced by the bilateral filtering method.Secondly,the machine learning algorithm based on an improved dic-tionary was used to build a two-layer dictionary.Meanwhile,the sparse representation algorithm was adopted to obtain a rough restored image of the first layer.Furthermore,the difference between the restored image of the first layer and the original image was calculated through the two-layer dictionary.After that,high-resolution samples were estab-lished and trained by the two-layer dictionary.Finally,the detail restoration of super-resolution images was achieved through the training structure.Experimental results prove that the peak signal-to-noise ratio can be maintained above 20dB,and the mean square error of detail restoration is less than 4×10-3.In addition,the structural similarity index is higher,and the training effect of high-resolution images is better.The feature contrast is obvious,and the detail infor-mation is particularly prominent.
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