Thermal-visible stereo matching at night based on Multi-Modal Autoencoder

Zhang, Quan; Li, Yiran; Yang, Le; Zhang, Yi; Li, Zechao; Chen, Xiaoyu; Han, Jing

doi:10.1016/j.infrared.2023.105010

Cited by 1 publication

(1 citation statement)

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“…It can compensate for the shortcomings of visible-light cameras, which struggle to function normally in special environments such as smoke, night-time, and strong light [5]. Limited by the performance constraints of infrared systems and disturbances from external environments, thermal infrared images often suffer from low overall contrast, poor visual effects, and blurred details compared to visible-light images [6]. These issues significantly impact the subsequent observation and target detection of thermal infrared images.…”

Section: Introductionmentioning

confidence: 99%

Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering

Li,

Wang,

et al. 2024

Fire

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Obtaining thermal infrared images with prominent details, high contrast, and minimal background noise has always been a focal point of infrared technology research. To address issues such as the blurriness of details and low contrast in thermal infrared images, an enhancement algorithm for thermal infrared images based on multi-scale guided filtering is proposed. This algorithm fully leverages the excellent edge-preserving characteristics of guided filtering and the multi-scale nature of the edge details in thermal infrared images. It uses multi-scale guided filtering to decompose each thermal infrared image into multiple scales of detail layers and a base layer. Then, CLAHE is employed to compress the grayscale and enhance the contrast of the base layer image. Then, detail-enhancement processing of the multi-scale detail layers is performed. Finally, the base layer and the multi-scale detail layers are linearly fused to obtain an enhanced thermal infrared image. Our experimental results indicate that, compared to other methods, the proposed method can effectively enhance image contrast and enrich image details, and has higher image quality and stronger scene adaptability.

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Section: Introductionmentioning

confidence: 99%