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Adaptive contrast enhancement of infrared images based on neighborhood conditional histogram

2021-10-27
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In order to improve the visual quality of thermal infrared images, an adaptive contrast enhancement method based on neighborhood conditional histogram is proposed. Existing block-based local contrast enhancement methods usually suffer from the problem of over-enhancement of smooth areas or loss of some details. To address these shortcomings, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid over-enhancement caused by the original histogram. The fragment redistribution histogram of contrast limited adaptive histogram equalization (CLAHE) is then 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 block artifacts. Finally, an optimized local contrast enhancement process is adopted to combine the global and local enhancement results to obtain the desired enhancement effect. The performance of the proposed method is experimentally evaluated and compared with five other methods. Qualitative and quantitative evaluation results show that the proposed method outperforms other block-based methods in local contrast enhancement, visual quality improvement, and noise suppression.

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