An effective super resolution image reconstruction using spatial entropy based normalized deep convolutional neural network

The main aim of Super Resolution (SR) is to obtain high quality image with high resolution from Low Resolution (LR) images. In numerous computer vision systems, the SR from a single image plays avital role.A major problem in Image SR reconstruction is to acquire a high-resolution (HR) image from a single LR image or multiple LR images which are having sub-pixel changes. Currently, learning based methodology is developed from the HR images from the LR images with better quality. This work presented a spatial entropy based normalization dependent deep convolutional neural network (SNDCN) for SR reconstruction with minimized artifact. Initially, the input LR images are pre-processed utilizing adaptive weighted linear smoothing (AWLS) filtering. Then the pre-processed smoothened images are undergoes to adaptive real time dual threshold (ARDT) technique for preserving the edges. Here, the edge preservation process utilizing the ARDT to enhance the quality of the image. Finally, SNDCN is utilized to construct SR images. The experimental outcomes of the presented technique proved that the varying performance measures attaining better super resolution image reconstruction than the existing methodologies. Additionally, the proposed method complete the reconstruction procedure within 18 s and obtain 34 dB as PSNR value for scaling factor 4 in which the outcome shows that significant of proposed methodology over existing methodologies.