No abstract

No abstract

The method of total variation is used as a significant and competent image prior model in the regularization based area of image processing. However, as the model showing total variation supports a piecewise steady explanation, this process is classify below high intensity noise in the level areas of the picture is often poor, and a small number of pseudo edges are produced. Under this work we build up a spatially adaptive total variation model. Initially, we extract the spatial data based on each and every pixel, then two filtering process are combined to control the collision of pseudo edges. It also includes, the spatial information weight is build and classified with k-means clustering, and the cluster controls the center value of regularization strength in every region. The tentative results, of both simulated as well as genuine datasets, exhibit that the projected methodology can effectively diminish the pseudo edges formed by the total variation regularization in the flat areas, and maintain the limited smoothness of the HR images. The proposed region based spatial information adaptive variation model can effectively reduce the cause of noise on the spatial data extraction and maintain strength with changes in the noise intensity in the SR process as compare to traditional pixel based spatial information adaptive methodology.

Abstract:In this paper we have compared Nun Subsampled Contourlet Transform and Wavelet Transform in image denoising on the basis of PSNR, RMSE and SSIM. In order to remove the image noise more effectively an image denoising algorithm based on Non Subsampled Contourlet Transform (NSCT) is proposed. The noisy image is first decomposed into multi-scale and multi-directional subbands by NSCT, and direction subbands of each high-pass component is processed by the new threshold function which is obtained by the Bayes threshold that based on stratified noise estimation. During the reconstruction, the low-pass subband constructed image is further denoisied by the bilateral filtering in the spatial domain. Experimental results compare with the result of image denoising using wavelet transform. It demonstrates that the proposed method can improve de-noising performance.

License plate recognition techniques have been successfully applied to the management of stolen cars, management of parking lots and traffic flow control. This study proposes a license plate based strategy for checking the annual inspection standing of motorcycles from images taken on the wayside and at selected inspection stations. Both a UMPC (Ultra Mobile Personal Computer) with a web camera and a desktop computer are used as hardware platforms in this paper a survey is being carried out in the field of Automatic license plate localization. Automatic license plate recognition (ALPR) is to extract vehicle license plate information from an image or a sequence of images. The extracted info can be used with or without a database in many applications like electronic payment systems, freeway and specific road observation systems for traffic surveillance.These modifications ought to considerably increase the effectiveness of our technique. Roman.