Abstract
In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.
Highlights
With the ability to convert the passively received infrared radiation into infrared images, infrared imaging systems have been widely used in military and civilian fields, for instance, thermal remote sensing, weapon guidance, night vision, fire detection, and disease diagnosis [1,2,3,4,5]
According to the scope of the mapping function of the contrast enhancement method, these methods can be classified into two categories: global contrast enhancement (GCE) and local contrast enhancement (LCE) [9]
As observed in the region marked by the red rectangle in the enhanced results of contrast-limited adaptive histogram equalization (CLAHE), balanced CLAHE and contrast enhancement (BCCE), and adjacent-blocks-based modification for local histogram equalization (ABMHE), there are non-uniform brightness regions in the sky around the tower crane
Summary
With the ability to convert the passively received infrared radiation into infrared images, infrared imaging systems have been widely used in military and civilian fields, for instance, thermal remote sensing, weapon guidance, night vision, fire detection, and disease diagnosis [1,2,3,4,5]. In addition to the block-based methods, the adaptive trilateral contrast enhancement (ATCE) [26] manipulates the contrast, sharpness, and intensity based on the modified histogram and the extracted feature of the input image Other models, such as the unsharp masking [27,28], retinex theory [29], and wavelet transform [30,31,32], can be utilized to improve the visual quality of infrared images. These LCE methods produce better local contrast enhanced result than GCE methods, they may still suffer from noise amplification, block artifacts, and/or loss of details.
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