Mosaic-SR: An Adaptive Multi-step Super-Resolution Method for Low-Resolution 2D Barcodes

Abstract: QR and Datamatrix codes are widely used in warehouse logistics and high-speed production pipelines. Still, distant or small barcodes often yield low-pixel-density images that are hard to read. Conventional solutions rely on costly hardware or enhanced lighting, raising expenses and potentially reducing depth of field. We propose Mosaic-SR, a multi-step, adaptive super-resolution (SR) method that devotes more computation to barcode regions than uniform backgrounds. For each patch, it predicts an uncertainty value to decide how many refinement steps are required. Our experiments show that Mosaic-SR surpasses state-of-the-art SR models on 2D barcode images, achieving higher PSNR and decoding rates in less time. All code and trained models are publicly available at https://github.com/Henvezz95/mosaic-sr.

Citation:

Vezzali, Enrico; Vorabbi, Lorenzo; Grana, Costantino; Bolelli, Federico "Mosaic-SR: An Adaptive Multi-step Super-Resolution Method for Low-Resolution 2D Barcodes" Proceedings of the 2025 IEEE International Conference on Image Processing, Anchorage, Alaska, USA, 14 - 17 Sep, 2025

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