# Adaptive Guided Filtering and Spectral-Entropy-Based Non-Uniformity Correction for High-Resolution Infrared Line-Scan Images

## TL;DR

This paper introduces a new lightweight method to correct stripe noise in high-resolution infrared images, improving quality for real-time applications.

## Contribution

A novel single-frame, reference-free non-uniformity correction method with adaptive filtering and spectral-entropy-based noise suppression.

## Key findings

- The method outperforms six state-of-the-art algorithms in simulated and real datasets.
- It achieves the fastest runtime while maintaining high performance across multiple metrics.

## Abstract

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks.