Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan, Minjie; Gu, Guohua; Qian, Wei; Ren, Kan; Chen, Qian; Maldague, Xavier

doi:10.3390/rs10050682

Cited by 50 publications

(32 citation statements)

References 42 publications

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“…The optimal solution α in this paper needs to be calculated by iteration. Furthermore, the average brightness of the output image is similarly corrected by the reference image using the PSO algorithm in the reference [6]. The YUV spatial image is obtained according to the Equation (1).…”

Section: Pso Algorithm For Calculating the Optimal Solution Of Histogmentioning

confidence: 99%

“…In order to verify the effectiveness of the brightness correction algorithm, this paper chooses (AHP-BC) [6], (AIP) [7], DHE [8], PHE [9] to compare with the brightness correction algorithm in this paper. Their comparison results are shown in Figure 12.…”

Section: Brightness Correction Experimentsmentioning

confidence: 99%

“…At present, people generally solve this problem from two aspects of brightness and color. In solving the difference in image brightness, Wan Mingjie et al proposed an infrared image enhancement method based on adaptive histogram partition and brightness correction (AHP-BC) [6]. Wang A. et al used atmospheric illumination prior (AIP) to correct the brightness of the image while maintaining the color of the haze image [7].…”

Section: Introductionmentioning

confidence: 99%

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Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Hao

Wang²,

2019

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At present, the identification of haze levels mostly relies on traditional measurement methods, the real-time operation and convenience of these methods are poor. This paper aims to realize the identification of haze levels based on the method of haze images processing. Therefore, this paper divides the haze images into five levels, and obtains the high-quality haze images in each level by the brightness correction of the optimization solution and the color correction of the feature matching. At the same time, in order to reduce the noise of the haze images, this article improved the Butterworth filter. Finally, based on the processed haze images, this paper uses the Faster R-CNN network to identify the haze levels. The results of multiple sets of comparison experiments demonstrate the accuracy of the study.

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Section: Pso Algorithm For Calculating the Optimal Solution Of Histogmentioning

confidence: 99%

Section: Brightness Correction Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Hao

Wang²,

2019

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“…In this paper, we set λ = 0.3 as a default value which is selected by experience. More analyses about η and λ are presented in Section 4.1.2. ε 2 is a constant that represents the value of the lower inflection point of Φ(x), and it will not obviously affect the output of Φ(x) as long as it is close to 0 (usually smaller than 0.0001) [38]. The balloon force α is a crucial parameter that directly determines the precision of segmentation and the convergence rate of the level set function.…”

Section: Parameter Settingmentioning

confidence: 99%

A Level Set Method for Infrared Image Segmentation Using Global and Local Information

Wan

Sun

et al. 2018

Remote Sensing

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Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.

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“…Among these methods, the plateau histogram equalization (PHE) [8,9], double plateaus histogram equalization (DPHE) [10], and adaptive double plateaus histogram equalization (ADPHE) [11] try to introduce one or two proper thresholds to avoid over-enhancement. Besides these plateau-based methods, some other methods such as the brightness preserving bi-histogram equalization (BBHE) [12], dualistic sub-image histogram equalization (DSIHE) [13], recursive mean-separate histogram equalization (RMSHE) [14], adaptive histogram segmentation (AHS) [15], and adaptive histogram partition (AHP) [16] address the over-enhancement by adaptively dividing the histogram into two or more partitions. Other methods such as the histogram modification framework (HMF) [17] and histogram specification (HS) [18] also gives better performance for avoiding over-enhancement.…”

Section: Introductionmentioning

confidence: 99%

Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

Sui

Kuang

et al. 2019

Remote Sensing

Self Cite

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In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.

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Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Cited by 50 publications

References 42 publications

Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

Haze Image Recognition Based on Brightness Optimization Feedback and Color Correction

A Level Set Method for Infrared Image Segmentation Using Global and Local Information

Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification

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