(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 14, No. 2, 2023 Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2 Saleh AlTakrouri 1 , Norliza Mohd Noor 2 , Norulhusna Ahmad 3 , Taghreed Justinia 4 , Sahnius Usman 5 Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia Kuala Lumpur Campus, Jalan Sultan Yahya Petra, Malaysia 1,2,3,5 King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia 4 Abstract—The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The super- resolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains. The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality. Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria. The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to ob- tain a better image quality. The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32. The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively. Keywords—Single image super-resolution (SISR); generative adversarial networks (GAN); convolutional neural networks (CNN); EfficientNetv2 I. I NTRODUCTION With the rapid development of information technology (IT) along with the boom of Internet technology, information processing based on image and signal is widely utilized by an enormous population, in which image processing is a crucial component of information processing. Here, the role of image super-resolution (SR) is significant when considering image processing-based applications [1], [2]. Image SR is the image transformation from low resolution (LR) to high resolution (HR) for obtaining an enhanced quality image. Medicine, agriculture, industry, and military applications utilize the SR technique due to its high practicability [3], [4]. While con- sidering artificial intelligence, the role of SR is crucial for performing various processes [5], like public security, remote sensing imaging, medical imaging, image compression, and so on using the single image SR criteria [6], [7]. The image resolution enhancement using the up-sampling process lacks texture details. The image transformed into the HR provides enormous information with finer details [8]. For example, the crime scene image offers plenty of evidence for investigating crime. Likewise, an image acquired from the satellite image undergoes various processing, like resource detection, object detection, and several other processing using the HR image [9]. While considering the medical application domain, the disease diagnosis is employed based on Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) Scan images with better resolution for providing accurate medication. Thus, the role of SR is crucial in image-processing application domains. The SR of the image is obtained from the LR image using three various categories: 1) learning-based approach, 2) reconstruction approach, and 3) interpolation approach [10]. Image resolution enhancement using interpolation is the earli- est method most researchers utilized and is easy to implement. Some of the interpolation techniques used to enhance the image’s quality are non-uniform sampling interpolation, Bicu- bic Interpolation, Bilinear Interpolation, and Nearest Neighbor Interpolation. In these approaches, the higher frequency details can be reconstructed through the linear characteristics of the approaches. The image SR using the interpolation approach provides a better outcome; still, the performance degrades with the scaling factor’s elevation [11]. Reconstruction based SR approach transforms the LR image by gathering the non- redundant details. The approaches with non-negativity, energy boundedness, support boundedness, and smoothness-based hy- pothetical constraints using the Projection onto Convex Set are one of the methods utilized for SR reconstruction. The slower convergence rate is the limitation of the reconstruction-based criteria and has many solutions [12], [13]. In addition, the reconstruction solution acquired at the final stage hang-on on the initial evaluation. Also, the performance is limited while considering reliable robustness and real-time modeling due to the inefficiency in handling the noise level reconstruction [14]. Finally, the third approach is the learning-based image transformation approach that enhances the quality of the image using machine learning and deep learning algorithms [15]. Nowadays, learning-based image quality enhancement is the widely utilized approach by researchers [16] due to better image perception. The learning of the network is employed in the learning- based approach of image SR for providing a high-quality reconstructed image. Here, for network learning, the high representation of the samples is utilized with variation in data for generalizing [17]. Thus, enormous data is acquired from various sources to obtain the required solution. While consider- ing the image taken from the remote sensing domain, the infor- mation collection is a challenging task due to the variations of image based on the factors like different sensors and locations along with the difference in the objects [18], [19], [20]. Thus, the network learned with the limited samples affects the model’s performance due to the poor generalization capability. Nowadays, the advent of deep learning models based on non- linear operation in artificial intelligence accumulates enormous www.ijacsa.thesai.org 879 | Page