Multiple Kernel Regression based on Image Resolution for JPEG Images

Image Processing

Now A days Image Super-Resolution is active research topic due its widespread use in many practical application. Recently Learning –Based Approach for Super–resolution (SR) has been used which generate favorable result. In this paper image super –resolution based on the multiple kernel regression is presented. This approach’s core is to learn the map between the space of high resolution image patches and the space of blurred high – resolution image patches. Which is the interpolation result generated from corresponding low-resolution image. Here using multiple kernel instead of single kernel for regression. Because choosing appropriate single kernel form image is difficult and time consuming rather than dividing image into multiple sub-band and each sub-band has own kernel. And Finally use Support Vector Regression (SVR) to fit the data in high dimensional feature space. The experimental result show that it achieve three time better quality of image than other.

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