NONE 20260421095230340 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON SIGNAL PROCESSING 1790-5052 2224-3488 Chamil Oshan Abeysekara Institute for Smart System Technologies, Universität Klagenfurt, AUSTRIA Binura Laksara Widanagama Institute for Smart System Technologies, Universität Klagenfurt, AUSTRIA Witesyavwirwa Vianney Kambale Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria, SOUTH AFRICA Mohamed El Bahnasawi Institute for Smart System Technologies, Universität Klagenfurt, AUSTRIA Mahmoud Hamed Institute for Smart System Technologies, Universität Klagenfurt, AUSTRIA Kyandoghere Kyamakya Institute for Smart System Technologies, Universität Klagenfurt, AUSTRIA Single Image Super-Resolution (SISR) reconstructs high-resolution (HR) images from low-resolution (LR) inputs. Although deep networks achieve strong results, many incur high computational cost and blur fine textures. We present an efficient deep autoencoder for SISR: an encoder extracts compact LR features, and a decoder synthesizes the HR output. To enhance detail recovery, we incorporate skip connections and residual learning within the reconstruction path. Evaluations on the DIV2K dataset show that the proposed model attains high PSNR and SSIM and surpasses several benchmark methods in reconstruction quality. Ablation studies confirm that each enhancement contributes to sharper edges and more consistent texture reproduction across diverse scenes and images. Compared with lightweight architectures such as FSRCNN, our approach uses moderately more parameters and runs slower on CPUs, yet produces clearly improved visual fidelity. 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