Single infrared image enhancement using a deep convolutional neural network

Kuang, Xiaodong; Sui, Xiubao; Liu, Yuan; Chen, Qian; Gu, Guohua

doi:10.1016/j.neucom.2018.11.081

Cited by 91 publications

(49 citation statements)

References 12 publications

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“…The image verifies the feasibility of the network and that it achieves good visual results. Then, quantitative methods are used to demonstrate the advantages of the proposed algorithm through comparing the Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of images with those of other two kinds of widely used algorithms: one is traditional image enhancement algorithms based on parameters, such as the CLAHE (Contrast-Limited Adaptive Histogram Equalization) algorithm [15], the SSR (Single-Scale Retinex) algorithm [19], the ABC (Artificial Bee Colony) algorithm [20] and the DOCS (Distance Oriented Cuckoo Search) algorithm [21], and the other is deep learning based image enhancement algorithms, for instance the CAEN (Convolutional Auto-encoder Network) algorithm [24], the DCNN (Deep Convolutional Neural Networks) algorithm [25], the DRF (Deep Residual Framework )algorithm [26] and DHN (Deep Hybrid Network) algorithm [27]. In addition, an ablation experiment on the convolution kernel size is conduct to indicate the advantage of proposed network in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…Wang et al proposed a framework called CAENet (convolutional auto-encoder network) which combined a low light processing module with a network training module [24]. Kuang et al proposed a deep learning method for single infrared image enhancement, and the conditional generative adversarial networks were incorporated into the optimization framework to avoid the background noise being amplified [25]. Liu et al proposed an underwater image enhancement solution through a deep residual framework to solve the problems of low contrast, blurred details and color distortion of the original underwater images [26].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

Wang

2020

IEEE Access

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Image enhancement is commonly used in digital image processing applications. Through image enhancement, the features of the target object are enhanced to make it easier to identify. In order to realize image enhancement, a neural network with a trapezoidal convolution kernel is proposed in this paper. First, weak contrast images are generated using the images in SIDD, DND and RENOIR, which are public datasets used for image denoising. On this basis, pixel distribution characteristics of the R, G and B channels of the weak contrast images are analyzed, and the histogram distribution of the images is given, which can be used to determine the size of the network convolution kernels. Then, a multi-layer convolution neural network is constructed, which uses the original image as an input and outputs the noise map. This network includes convolution kernels of different sizes for the three channels of the original images, and the convolution kernel size is determined by calculating the mean square deviation of the pixels of the corresponding channel. The proposed algorithm provides visually pleasing contrast enhancement. In this paper, a public dataset for image enhancement and actual captured images are separately assessed, both the contrast experiment and ablation experiment results show that the algorithm proposed in this paper can achieve a higher Peak Signal to Noise Ratio (PSNR) and a higher Structural Similarity Index (SSIM) than other image enhancement algorithms. INDEX TERMS Enhancement algorithm, trapezoidal convolution Kernel, image contrast, feature analysis, neural network.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

Wang

2020

IEEE Access

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“…Recently, Kandhway and Bhandari [43] proposed an adaptive thresholding based sub-histogram equalization method to preserve the mean brightness and improve the contrast enhancement. In [44], Kuang et al suggested a neural network to enhance the image contrast and reveal the image details. They also incorporated the conditional generative adversarial network for further enhancement of image contrast and to avoid the background noise amplification.…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

et al. 2019

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Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.

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“…Several approaches have been proposed to improve a low-quality thermal image by only leveraging software-based techniques. The approaches include detail enhancement (super-resolution) [10][11][12][13][14][15], noise reduction [16][17][18][19], contrast enhancement [20][21][22][23], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Ensemble Learning for Thermal Image Enhancement

Ban

Lee

2021

Applied Sciences

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In this study, we propose a multi-scale ensemble learning method for thermal image enhancement in different image scale conditions based on convolutional neural networks. Incorporating the multiple scales of thermal images has been a tricky task so that methods have been individually trained and evaluated for each scale. However, this leads to the limitation that a network properly operates on a specific scale. To address this issue, a novel parallel architecture leveraging the confidence maps of multiple scales have been introduced to train a network that operates well in varying scale conditions. The experimental results show that our proposed method outperforms the conventional thermal image enhancement methods. The evaluation is presented both quantitatively and qualitatively.

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Single infrared image enhancement using a deep convolutional neural network

Cited by 91 publications

References 12 publications

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Multi-Scale Ensemble Learning for Thermal Image Enhancement

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