Հրապարակման մանրամասներ:
Ամսագրի կամ հրապարակման վերնագիր:
Հրապարակման ամսաթիվ:
Հատոր:
ISSN:
Լրացուցիչ տեղեկություն:
սեղմիր այստեղ կապին հետևելու համար
Վերնագիր:
Mamba-based Thermal Image Dehazing
Ստեղծողը:
Խորագիր:
Mathematical cybernetics ; Computer science
Չվերահսկվող բանալի բառեր:
Thermal Image ; Image Dehazing ; Thermal Image Enhancement ; Mamba
Ծածկույթ:
Ամփոփում:
Atmospheric phenomena such as rain, snow, urban, forest fires, and artificial disasters can degrade image quality across various applications, including transportation, driver assistance systems, surveillance, military, and remote sensing. Image dehazing techniques aim to reduce the effects of haze, dust, fog, and other atmospheric distortions, enhancing image quality for better performance in computer vision tasks. Haze not only obscures details but also reduces contrast and color fidelity, significantly impacting the accuracy of computer vision (CV) models used in object detection, image classification, and segmentation. While thermal infrared (TIR) imaging is often favored for long-range surveillance and remote sensing due to its resilience to haze, atmospheric conditions can still degrade TIR image quality, especially in extreme environments. This paper introduces MTIE-Net, a novel Mamba-based network for enhancing thermal images degraded by atmospheric phenomena like haze and smoke. MTIE-Net leverages the Enhancement and Denoising State Space Model (EDSSM), which combines convolutional neural networks with state-space modeling for effective denoising and enhancement. We generate synthetic hazy images and employ domain-specific transformations tailored to thermal image characteristics to improve training in low-visibility conditions. Our key contributions include using the Mamba architecture with 2D Selective Scanning for thermal image enhancement, developing a specialized Enhancement and Denoising module, and creating a labeled thermal dataset simulating heavy haze. Evaluated on the M3DF dataset of long-range thermal images, MTIE-Net surpasses state-of-the-art methods in both quantitative metrics (PSNR, SSIM) and qualitative assessments of visual clarity and edge preservation. This advancement significantly improves the reliability and accuracy of critical systems used in remote sensing, surveillance, and autonomous operations by enhancing image quality in challenging environments.