Edge Craft Odyssey: Navigating guided super-resolution with a fast, precise, and lightweight network

Abstract

Thermal imaging technology is exceptionally valuable in environments where visibility is limited or nonexistent. However, the high cost and technological limitations of high-resolution thermal imaging sensors restrict their widespread use. Many thermal cameras are now paired with high-resolution visible cameras, which can help improve low-resolution thermal images. However, aligning thermal and visible images is challenging due to differences in their spectral ranges, making pixel-wise alignment difficult. Therefore, we present the Edge Craft Odyssey Network (ECONet), a lightweight transformer-based network designed for Guided Thermal Super-Resolution (GTSR) to address these challenges. Our approach introduces a Progressive Edge Prediction module that extracts edge features from visible images using an adaptive threshold within our innovative Edge-Weighted Gradient Blending technique. This technique provides precise control over the blending intensity between low-resolution thermal and visible images. Additionally, we introduce a lightweight Cascade Deep Feature Extractor that focuses on efficient feature extraction and edge weight highlighting, enhancing the representation of high-frequency details. Experimental results show that ECONet outperforms state-of-the-art methods across various datasets while maintaining a relatively low computational and memory requirements. ECONet improves performance by up to 0.20 to 1.3 dB over existing methods and generates super-resolved images in a fraction of a second, approximately $91\%$ faster than the other methods. The code is available at https://github.com/Rm1n90/ECONet.