Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Minjie Wan,Weixian Qian,Xavier Maldague,Guohua Gu,Kan Ren,Qian Chen

doi:10.3390/rs10050682

Abstract

Infrared image enhancement is a crucial pre-processing technique in intelligent urban surveillance systems for Smart City applications. Existing grayscale mapping-based algorithms always suffer from over-enhancement of the background, noise amplification, and brightness distortion. To cope with these problems, an infrared image enhancement method based on adaptive histogram partition and brightness correction is proposed. First, the grayscale histogram is adaptively segmented into several sub-histograms by a locally weighted scatter plot smoothing algorithm and local minima examination. Then, the fore-and background sub-histograms are distinguished according to a proposed metric called grayscale density. The foreground sub-histograms are equalized using a local contrast weighted distribution for the purpose of enhancing the local details, while the background sub-histograms maintain the corresponding proportions of the whole dynamic range in order to avoid over-enhancement. Meanwhile, a visual correction factor considering the property of human vision is designed to reduce the effect of noise during the procedure of grayscale re-mapping. Lastly, particle swarm optimization is used to correct the mean brightness of the output by virtue of a reference image. Both qualitative and quantitative evaluations implemented on real infrared images demonstrate the superiority of our method when compared with other conventional methods.

Highlights

Infrared (IR) imaging has been extensively applied in Smart City applications [1,2], e.g., scene surveillance, goods sorting, and fire prevention due to its unique ability to receive IR rays (780 nm–300 μm) which cannot be perceived by human eyes
To aRdemdotreeSsenss.t2h01e8,a1f0o, 6r8e2-mentioned problems in the existing methods, we focus on inve3 softi3g4 ating an effective IR image enhancement algorithm based on adaptive histogram partition and brightness correction
The reason why the δ values of the ‘computer’ image are relatively large is that the brightness of the input itself is much higher than the reference image, so the mean brightness of the enhanced image cannot approach the reference even if m1 = 0

Summary

Introduction

Infrared (IR) imaging has been extensively applied in Smart City applications [1,2], e.g., scene surveillance, goods sorting, and fire prevention due to its unique ability to receive IR rays (780 nm–300 μm) which cannot be perceived by human eyes. Global histogram equalization (GHE) is the simplest one, in which a transfer function formulated by the cumulative density function (CDF) is utilized to re-calculate the grayscale histogram so that the overall histogram distribution is forced to be flattened out and accounts for a broader dynamic range, exaggerating the global contrast to the greatest extent [5] To this end, grayscales with high probability distribution function (PDF) values (usually belonging to the background) will occupy the most intensity ranges and be dramatically enhanced, while those with low PDF values (usually belonging to target regions) will be greatly suppressed or even lost. In an even worse scenario, the noise existing in background may be hugely amplified due to over-enhancement

Methods

Results

Conclusion