aspg-3-2567-1791943322 20261014020202 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 1 H. I. Wahhab College of Computer Science & Information Technology, University of Kerbala, Kerbala, 56002, Iraq A. N. Alanssari Faculty of Basic education, Department of Mathematics, University of Kufa, Najaf, Iraq Ahmed L. Khalaf College of Engineering, Al-Iraqia University, Baghdad, 10054, Iraq Ravi Sekhar Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra,India Pritesh Shah Symbiosis Institute of Technology (SIT) Pune Campus, Symbiosis International (Deemed University) (SIU), Pune, 412115, Maharashtra,India Jamal F. Tawfeq Department of Medical Instrumentation Technical Engineering, Medical Technical College, Al-Farahidi University, Baghdad, 10070, Iraq The emergence of low-cost red, green, and blue (RGB) cameras has significantly impacted various computer vision tasks. However, these cameras often produce depth maps with limited object details, noise, and missing information. These limitations can adversely affect the quality of 3D reconstruction and the accuracy of camera trajectory estimation. Additionally, existing depth refinement methods struggle to distinguish shape from complex albedo, leading to visible artifacts in the refined depth maps. In this paper, we address these challenges by proposing two novel methods based on the theory of photometric stereo. The first method, the RGB ratio model, tackles the nonlinearity problem present in previous approaches and provides a closed-form solution. The second method, the robust multi-light model, overcomes the limitations of existing depth refinement methods by accurately estimating shape from imperfect depth data without relying on regularization. Furthermore, we demonstrate the effectiveness of combining these methods with image super-resolution to obtain high-quality, high-resolution depth maps. Through quantitative and qualitative experiments, we validate the robustness and effectiveness of our techniques in improving shape transformations for RGB cameras. 2024 144 156 2567 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150112 https://americaspg.com/journal/3/article/2567 https://americaspg.com/storage/71709908686.pdf