Super-resolution images are highly desired when employed for numerous analytical purposes and obviously because of their superior attractive visual effect. To create a High-Resolution (HR) image from one or more Low-Resolution (LR) images, an image super-resolution technique is used. When dealing with natural environments and settings, it is not always easy to access high-resolution images. The main barriers to the same are limitations in acquisition methods. In the domains of forensics investigation, remote sensing, digital monitoring, and medical imaging high-resolution images are always required. Modern methods using deep learning models have improved performance when compared to classic image processing methods. Using a convolution auto-encoder architecture with six parallel skip connections, a novel technique for improving lower solution natural images of size 256 × 256 to high-resolution images is proposed in this paper. The use of parallel skip connections between the encoder and decoder allows the network to reconstruct high-resolution images while still extracting relevant features from low-resolution images. Furthermore, the network is skillfully tuned using the right filters and regularization techniques. To create high-resolution output images, the decoder component makes use of the features' reduced representation in latent space. The model was evaluated using DIV 2K, CARS DATA, Set5, Set14, and the General data set after being trained using various data sets like CARS DATA and DIV 2K. Peak Signal to Noise Ratio, Structural Similarity Index, Mean Squared Error, and model behavior on numerous data sets are used to compare the proposed method to various current methods. Results reveal that the suggested model works better than the existing method and is reliable.