IR275K: A Benchmark for Infrared Multi-Frame Super-Resolution Toward Efficient Remote Sensing

AI in healthcare

Published: arXiv: 2607.22380v1

Authors

Jie Deng Heyang Wang Changxin Wang Junkai Shen Hongyi Chen Zhiping He Hongxing Qi Xudong Zhang Jianyu Wang

Abstract

Efficient processing is becoming increasingly important in infrared remote sensing, where satellite constellations produce large volumes of observations under constrained detector resolution, power, and downlink bandwidth. Multi-frame super-resolution (MFSR) offers a software-based route to spatial enhancement, but its evaluation in infrared sensing remains fragmented across private datasets and ad-hoc protocols. Existing benchmarks do not explicitly capture the thermal contrast, sensor noise, weak texture, and platform-induced frame-to-frame variation that characterize infrared video. We introduce IR275K, a curated benchmark containing 594 infrared video sequences and 275,196 frames. It provides sequence-level train/validation/test splits and a reproducible X4 evaluation protocol. As an initial architectural probe, we further evaluate CGMamba, a lightweight state-space model with 10.90M parameters and 112.14G FLOPs. CGMamba combines 2D rotary position encoding (2D~RoPE) with center-guided cross-Mamba (CGCM) fusion for implicit multi-frame reconstruction. It achieves 33.19dB PSNR, outperforming infrared single-image super-resolution references by 0.35--0.52~dB at substantially lower computational cost. Ablation results show that removing 2D~RoPE from CGCM causes a 1.53dB drop and severe grid-like artifacts. This indicates that explicit spatial anchoring is critical for stabilizing SSM-based cross-frame gating under infrared conditions. IR275K provides a reproducible foundation for accuracy--efficiency evaluation of infrared MFSR methods, while the architectural analysis offers a concrete starting point for spatially aware SSM design under resource-constrained infrared sensing. Dataset and evaluation resources are available at: https://github.com/InfraRecon7/IR275K.

Paper Summary

Problem

The main problem addressed in this research paper is the need for efficient processing in infrared remote sensing. With the increasing volume of infrared video data from satellite constellations, there is a growing need for methods that can enhance image quality without upgrading detectors or increasing downlink demand. However, existing benchmarks for multi-frame super-resolution (MFSR) do not capture the unique conditions of infrared sensing, such as weak thermal contrast, sensor noise, and frame-to-frame variation.

Key Innovation

The key innovation of this work is the introduction of IR275K, a benchmark dataset specifically designed for infrared MFSR. IR275K contains 594 curated infrared video sequences with standardized sequence-level splits and a reproducible ×4 evaluation protocol. This benchmark provides a common ground for evaluating MFSR methods in terms of reconstruction quality, computational efficiency, and reproducibility. Additionally, the paper presents a lightweight state-space model called CGMamba, which achieves state-of-the-art performance in infrared MFSR while being computationally efficient.

Practical Impact

The practical impact of this research is significant. IR275K provides a reproducible foundation for evaluating MFSR methods in infrared remote sensing, allowing researchers to compare and improve their approaches. The CGMamba architecture, which is designed to be efficient and spatially grounded, offers a starting point for developing more accurate and efficient MFSR methods. This can lead to improved image quality and reduced computational costs in various applications, such as maritime surveillance, emergency response, and environmental monitoring.

Analogy / Intuitive Explanation

Imagine trying to enhance a low-resolution image of a landscape taken from a moving car. The image is blurry and noisy, with weak contrast between different features. To improve the image, you need to combine multiple frames taken from the car, but each frame has its own set of challenges, such as sensor noise and frame-to-frame variation. IR275K is like a training dataset that provides a collection of such images, along with a set of rules for evaluating how well different methods can enhance them. The CGMamba architecture is like a specialized tool that can take advantage of these rules to produce high-quality images with minimal computational cost.

Paper Information

Categories:

cs.CV

Published Date:

arXiv ID:

2607.22380v1

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