Open AccessInfrared imaging enhancement through local window‐based saliency extraction with spatial weight
Author(s) -
Li Tong,
Zhao Jufeng,
Wu Xiaohui,
Mao Haifeng,
Cui Guangmang
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12276
Subject(s) - computer science , window (computing) , artificial intelligence , infrared , scale (ratio) , pattern recognition (psychology) , fusion , contrast (vision) , image enhancement , extraction (chemistry) , image (mathematics) , feature extraction , reduction (mathematics) , contrast enhancement , computer vision , mathematics , optics , chemistry , physics , linguistics , philosophy , chromatography , quantum mechanics , operating system , medicine , geometry , magnetic resonance imaging , radiology
Abstract Infrared image enhancement is an effective way to solve contrast reduction or details degradation in infrared imagery. An infrared enhancement approach based on local saliency extraction is proposed here. First, saliency maps are extracted within a local window by combining spatial weight. Second, with the change of the window size, potential targets and details in different sizes can be extracted. Considering window sizes as the scales, the saliency maps are obtained and infrared images are enhanced at different scales, and finally, multi‐scale fusion is used to achieve the enhancement. Eight popular infrared enhancement approaches are introduced for comparison. Subjective qualitative observation experiments show that our strategy based on local saliency analysis and multi‐scale fusion can well extract potential targets and areas of varying size from the source images with different sizes, to obtain a good enhancement effect. Meanwhile, we introduce six objective evaluation methods to measure the results, and the evaluation data to prove the effectiveness of the proposed algorithm. The experiments also indicate the real‐time processing capability of the proposed method. The proposed method is finally well applied in the hardware system and shows its good performance.