Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single Image Super Resolution

The super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier Graphics Processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. The proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak Signal-to-Noise Ratio. The dataset extracted from eecs.berkeley.edu(http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/BSR_bsds500.tgz). The dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 * 300 pixels. The proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. The processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak Signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak Signal to Noise Ratio of 48 dB when compared to other optimized output layers.