IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 161 10.51583/IJLTEMAS.2026.150700164 Evaluation Enhancement of Low Resolution Natural Images Using Deep Learning Based Super Resolution Techniques Shashidhara. B Dr. Associate Professor, GFGC, Shimoga India 15 7 2152 2159 17 08 2026 22 08 2026 26 08 2026 Image Super-Resolution Deep Learning Convolutional Neural Network (CNN) Transformer Generative Adversarial Network (GAN) Abstract

Image super-resolution is an important image processing task that aims to reconstruct a high resolution image from a low resolution input. Conventional interpolation methods often produce blurred edges, loss of texture, and pixelated outputs, particularly when images contain noise, compression artefacts, or complex real world degradations. This work proposes a hybrid CNN Transformer GAN framework for natural image super resolution. The CNN component extracts local features such as edges, patterns, and fine textures; the Transformer component captures long range dependencies and preserves global image structure; and the Generative Adversarial Network (GAN) component improves perceptual quality by generating sharper and more realistic visual details. The proposed model supports 2X, 4X, and 8X upscaling, with 4X selected as the primary evaluation scale because it provides a practical balance between image quality and computational complexity. The system is trained using degraded low-resolution images produced through blur, noise, JPEG compression, and downsampling operations. Performance is evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Mean Squared Error (MSE), and visual quality analysis. The hybrid architecture is expected to improve edge sharpness, preserve structural details, reduce visual artefacts, and generate natural looking high-resolution outputs compared with conventional CNN and GAN based approaches. The proposed framework can support applications in photo restoration, mobile imaging, surveillance, e-commerce, digital archives, and web image enhancement.

 References Aakerberg, A., Johansen, A. S., Nasrollahi, K., & Moeslund, T. B. (2022). Semantic segmentation guided real-world super-resolution. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW). https://doi.org/10.1109/WACVW54805.2022.00051 Xu, X., Wei, P., Chen, W., Liu, Y., Mao, M., Lin, L., & Li, G. (2022). Dual adversarial adaptation for cross-device real-world image super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5657–5666. Liu, A., Liu, Y., Gu, J., Qiao, Y., & Dong, C. (2023). Blind image super-resolution: A survey and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5), 5461–5480. Wei, P., Xie, Z., Li, G., & Lin, L. (2023). Taylor neural network for real-world image super-resolution. IEEE Transactions on Image Processing. https://doi.org/10.1109/TIP.2023.3255107 Zhang, W., Li, X., Shi, G., Chen, X., Qiao, Y., Zhang, X., Wu, X.-M., & Dong, C. (2023). Real-world image super-resolution as multi-task learning. In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). Wang, X., Liang, Z., Wang, Y., Yang, H., An, W., & Guo, Y. (2022). Real-world light field image super-resolution via degradation modulation. Wei, P., Sun, Y., Guo, X., Liu, C., Li, G., Chen, J., Ji, X., & Lin, L. (2023). Towards real-world burst image super-resolution: Benchmark and method. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 13187–13196. Chen, X., Wang, X., Zhang, W., Kong, X., Qiao, Y., Zhou, J., & Dong, C. (2023). HAT: Hybrid attention transformer for image restoration. Tian, C., Zhang, X., Zhu, Q., Zhang, B., & Lin, J. C.-W. (2022). Generative adversarial networks for image super-resolution: A survey. Lin, W. (2022). Single image super-resolution quality assessment: A real-world dataset, subjective studies, and an objective metric. IEEE Transactions on Image Processing, 31, 2279–2294. https://doi.org/10.1109/TIP.2022.3154588 Deviyani, S., Hoplamaz, F., & Paul, M. (2022). How real is real: Evaluating the robustness of real-world super-resolution. Babaguchi, N., & Aizawa, K. (2022). Robust real-world image super-resolution against adversarial attacks.