A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only

• URL: http://arxiv.org/abs/2503.23265v1
• Date: Sun, 30 Mar 2025 00:52:26 GMT
• Title: A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
• Authors: Björn Möller, Lucas Görnhardt, Tim Fingscheidt,
• Abstract summary: Transformer architectures prominently lead single-image super-resolution (SISR) benchmarks, reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts.<n>Their strong representative power, however, comes with a higher demand for training data compared to convolutional neural networks (CNNs)<n>In this work, we are the first to utilize a lightweight vision transformer model with LR-only training methods addressing the unsupervised SISR LR-only benchmark.
• Score: 22.127873567034825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformer architectures prominently lead single-image super-resolution (SISR) benchmarks, reconstructing high-resolution (HR) images from their low-resolution (LR) counterparts. Their strong representative power, however, comes with a higher demand for training data compared to convolutional neural networks (CNNs). For many real-world SR applications, the availability of high-quality HR training images is not given, sparking interest in LR-only training methods. The LR-only SISR benchmark mimics this condition by allowing only low-resolution (LR) images for model training. For a 4x super-resolution, this effectively reduces the amount of available training data to 6.25% of the HR image pixels, which puts the employment of a data-hungry transformer model into question. In this work, we are the first to utilize a lightweight vision transformer model with LR-only training methods addressing the unsupervised SISR LR-only benchmark. We adopt and configure a recent LR-only training method from microscopy image super-resolution to macroscopic real-world data, resulting in our multi-scale training method for bicubic degradation (MSTbic). Furthermore, we compare it with reference methods and prove its effectiveness both for a transformer and a CNN model. We evaluate on the classic SR benchmark datasets Set5, Set14, BSD100, Urban100, and Manga109, and show superior performance over state-of-the-art (so far: CNN-based) LR-only SISR methods. The code is available on GitHub: https://github.com/ifnspaml/SuperResolutionMultiscaleTraining.

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