Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

Li, Tong; Zhao, Jincheng; Wu, Xiaohui; Mao, Haidan; Cui, Guangmang

doi:10.1049/ipr2.12276

Cited by 3 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Infrared imaging is the process of converting the infrared radiation and background information of the target into infrared images [1]. Infrared imaging technology is a new type of high-tech that is very useful for both civil and military.…”

Section: Introductionmentioning

confidence: 99%

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

Zheng

Chang

Wang

et al. 2023

Advances in Mathematical Physics

View full text Add to dashboard Cite

The correlation filtering algorithm of infrared spectral data for dim and small target tracking is studied to improve the tracking accuracy of small and weak targets and to track small and weak targets in real time. After the image noise reduction processing by the mean shift filtering algorithm, the infrared small and weak target image data model is constructed by using the denoised infrared small and weak target image. And the brightness value and position of unknown small and weak targets are obtained. The tracking and measurement model of small and weak targets is built. The joint probabilistic data association algorithm is used to calculate the probability that each measurement is associated with its possible source targets, and the particle filter is used to update the tracking status of small and weak targets to achieve real-time tracking of small and weak targets. The experimental results show that the algorithm can enhance the edge contour information of small and weak images, so as to accurately track small and weak targets moving in any track, and has good real-time tracking and accuracy. There is a small deviation between the tracking track of weak and small targets tracked by the algorithm and the actual track, and the root mean square difference of tracking weak and small targets is within 2 pixels. In addition, the detection probability of detecting weak and small targets is less affected by SNR environmental factors.

show abstract

Section: Introductionmentioning

confidence: 99%

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

Zheng

Chang

Wang

et al. 2023

Advances in Mathematical Physics

View full text Add to dashboard Cite

show abstract

“…The fourth category is based on multi-scale and multi-level detail enhancement methods [ 13 ]. To fully extract the tiny details in the image, researchers have proposed related methods, such as the multi-scale feature extraction based on saliency of local windows [ 14 ], multi-scale sequential toggle operator using opening and closing as primitives [ 15 ], and multi-level image enhancement [ 16 ]. Voronin, V et al proposed an algorithm based on multi-scale alpha-rooting processing, which is enhanced by segmenting image blocks [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Jiang

Dong

Liang

2022

Sensors

View full text Add to dashboard Cite

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

show abstract

Contrast enhancement algorithm for infrared images based on multiscale difference of morphological reconstruction

Li,

Siddique

et al. 2025

Optics & Laser Technology

View full text Add to dashboard Cite

Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

Cited by 3 publications

References 34 publications

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination

Contrast enhancement algorithm for infrared images based on multiscale difference of morphological reconstruction

Contact Info

Product

Resources

About