A novel picture super-resolution (SR) technique based on a Convolution Neural Network (CNN) is being developed as part of this project's research. When learning the feature extraction, upsampling, and high-resolution (HR) reconstruction modules at the same time, a deep convolutional neural network (CNN) is created that can be used to rebuild pictures from any source and is completely trainable. If, on the other hand, you want to train a deep network in a straight line from start to end, this is time-consuming and may provide sub-optimal results since it takes a longer time to converge than other strategies. According to our results, an ensemble of deep and shallow networks should be trained at the same time in order to overcome this difficulty