Unified framework of dense convolution neural network for image super resolution

In the last few years, Image Enhancement is a very necessary task in computer vision and to enhance the image. Super Resolution (SR) technique has been used to enhance the resolution of image. Super Resolution is a technique which is used to convert High Resolution (HR) from Low Resolution Image (LR). In this paper, to understand SR problems and to design an efficient and robust model, Convolution neural network (CNN) is used. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on other datasets and acheived advance results as compared to existing works.