Two-dimensional barcodes (such as QR and Datamatrix) are widely used in warehouse logistics and high-speed production pipelines to automate product tracking. However, to handle various size packages, it happens frequently that small and high-resolution barcodes are challenging to decode. Conventional solutions address such challenges by utilizing expensive hardware (e.g. CPU, FPGA, ASIC) or powerful lighting sources, increasing the costs of the system. This study introduces a multi-step, scalable, and adaptive super-resolution (SR) method that focuses primarily on the areas where barcodes are present and minimizes the computational burden on the uniform regions of the background. Our approach achieves superior image quality by dynamically determining the required refinement steps for each region of the image analyzed. Experiments demonstrate that our method outperforms the state-of-the-art SR models on barcode images, reaching higher PSNR and decoding rates while reducing the latency.