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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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WMRNet: Wavelet Mamba With Reversible Structure for Infrared Small Target Detection.

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    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    This study introduces the Wavelet Mamba with Reversible Structure Network (WMRNet) for infrared small target detection (IRSTD). WMRNet effectively reduces aliasing effects, improving the identification of small targets in infrared images.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Infrared small target detection (IRSTD) is crucial for applications like maritime rescue and early warning systems.
    • Aliasing effects from undersampling severely hinder small target identification in infrared imagery.
    • Existing methods struggle with segmenting small targets due to background noise and aliasing.

    Purpose of the Study:

    • To propose a novel network, Wavelet Mamba with Reversible Structure Network (WMRNet), for enhanced infrared small target detection.
    • To address the challenge of aliasing effects that impede the accurate detection of small infrared targets.
    • To improve the segmentation of small targets by minimizing frequency interference and refining target edges.

    Main Methods:

    • The proposed WMRNet integrates a Discrete Wavelet Mamba (DW-Mamba) module and a Third-order Difference Equation guided Reversible (TDE-Rev) structure.
    • DW-Mamba utilizes Discrete Wavelet Transform for multi-subband decomposition and integrates it into a state space model to reduce background aliasing.
    • TDE-Rev refines target edges using a neural structure from second-order difference equations and a reversible structure to suppress edge aliasing.

    Main Results:

    • WMRNet effectively minimizes frequency interference and preserves global context, significantly reducing background aliasing.
    • The TDE-Rev structure successfully suppresses edge aliasing effects by enhancing feature interactions and refining target edges.
    • Experiments on IRSTD-1k and SIRST datasets show WMRNet surpasses current state-of-the-art methods in infrared small target detection.

    Conclusions:

    • The WMRNet presents a robust solution for infrared small target detection, overcoming limitations posed by aliasing.
    • The integration of wavelet transforms and reversible structures offers a promising direction for future research in IRSTD.
    • WMRNet demonstrates superior performance, highlighting its potential for real-world applications requiring reliable small target identification.