Infrared image enhancement based on atmospheric scattering model and histogram equalization

Li, Yang; Zhang, Yunfeng; Geng, Aihui; Cao, Lei; Chen, Juan

doi:10.1016/j.optlastec.2016.03.017

Cited by 35 publications

(11 citation statements)

References 13 publications

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“…Feature extraction is an important step in designing an automatic diagnostic system 71 . Statistical texture features have been reported as useful for the classification of retinal images by analysing the spatial distribution of the gray levels, computing the local features and obtaining a statistical distribution of the local features.…”

Section: Methodsmentioning

confidence: 99%

Infrared retinal images for flashless detection of macular edema

Ajaz

Kumar

2020

Sci Rep

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This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (A Z) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

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Section: Methodsmentioning

confidence: 99%

Infrared retinal images for flashless detection of macular edema

Ajaz

Kumar

2020

Sci Rep

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“…The other way to haze images is based on the atmospheric light scattering model, which is also an important step in the CNN dehazing algorithm [5]. Guo et al [6] proposed an effective method based on the atmospheric scattering model [7], [8]:…”

Section: A Model-based Approachmentioning

confidence: 99%

Image Hazing Algorithm Based on Generative Adversarial Networks

et al. 2020

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Haze is an important factor in photography with a special aesthetic, emotional, or compositional meaning, an image hazing method is proposed based on generative adversarial network. The proposed network consists of two parts: the generator has a symmetric encoder and decoder structure with skip connection, which is used to generate hazy images; and the discriminator is a global fully convolutional network, which is used to identify the reality of the generated hazy images. The haze-free image is used as the input of the generative network to obtain a corresponding hazy image. Then the discriminative network judges the similarity between the original haze-free image and the corresponding hazy image to optimize the network parameters, which means that all the parameters of the entire network can be automatically learned through training. The loss function of the network is the combination of the GAN(Generative Adversarial Networks) loss and the REG(Regression) loss with a coefficient λ. We design a REG loss which includes feature loss loss FL and image loss loss IL. By calculating the loss, a model mapping the relationship between the two corresponding pixels from the original image and the synthetic hazy image respectively is well trained. In the last part of the paper, we have shown some impressing synthetic hazy images obtained by our model as well as analyzing the effects on images with different scenes. Moreover, we have contrasted the experimental results of our algorithm with that of other classical ones. Experiments demonstrate that the proposed algorithm has a better performance than other state-of-the-art methods on both virtual-scene and real-world images qualitatively and quantitatively. INDEX TERMS Generative adversarial networks, image hazing, regression loss, mapping from pixels to pixels.

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“…Inspired by previous works [21][22][23], the strategy of segmenting a histogram via local minima is adopted in this paper. Unlike the conventional methods, we focus on improving its robustness to those spikes existing in the histogram.…”

Section: Adaptive Segmentation Of Input Grayscale Histogrammentioning

confidence: 99%

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

Wan

Qian

et al. 2018

Remote Sensing

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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.

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Infrared image enhancement based on atmospheric scattering model and histogram equalization

Cited by 35 publications

References 13 publications

Infrared retinal images for flashless detection of macular edema

Infrared retinal images for flashless detection of macular edema

Image Hazing Algorithm Based on Generative Adversarial Networks

Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction

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