Super resolution techniques have emerged for different applications in various fields as HR photographs provide an abundance of information and helpful. Deep learning-based SR models have been rapidly developed over the past few years, and deep learning-based SR models are commonly found to deliver on the benchmarks of super resolution photos. This study proposes to use Wavelet-based high-resolution processing with Deep learning algorithms for super-resolution processing. The resolution of the HR images is changed to Low Resolution (LR) utilizing bicubic interpolation-based down sampling and up sampling before beginning the resolution step. It is also helpful to generate the four sub-bands of each image by employing the Wavelet lifting approach. Also, the residual picture is created by subtracting the LRSB from the HRSB. Training and testing are the two major phases in the suggested paradigm. During the training phase, Deep Convolutional Neural Network (DCNN) trains the residual image of all images by feeding LRSB as input and the residual image as target. This alternative view is in line with the idea that in testing, the LRSB query image is subject to Deep CNN, which returns the residual image to the neural network. The image that was created as a result of the summing of the residual image with LRSB image, along with an inverse wavelet lifting procedure, results in the final super resolution image. This work aims to improve the Deep CNN by modifying the Whale Optimization Algorithm (WOA) by altering the number of hidden layers and hidden neurons (PSNR). In the end, the proposed approach delivered equivalent outcomes to the other models.