RDCN-SR: Integrating regression model with deep convolutional networks for image super-resolution

The spatial resolution of the images captured by the optical components is very less and the image details are minimized due to problems, such as optical blurring, deviation in the lens and so on. Hence, the image resolution enhancing techniques have obtained more attention in recent years. This paper presents an image super-resolution (SR) method by integrating the deep convolutional network with the kernel regression model. At first, the low-resolution image is applied to the bi-cubic interpolation to increase the dimensions of the image based on the upscaling factor. Then, the image produced by the bi-cubic interpolation is applied to the deep convolutional network. Later, the high-resolution (HR) image obtained from the deep convolutional network is provided as the input to the kernel regression function to generate the enhanced high-resolution image. Finally, this paper analyses the performance of the proposed hybrid model for image super-resolution with the existing systems, such as Bicubic, SRCNN 2014, and SRCNN 2016 using PSNR. Experimental results show that the proposed model generates the enhanced HR image by achieving the higher PSNR value.