Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Fu, Chenglong; Lu, Fengli; Zhang, Xiaoxiao; Zhang, Guoying

doi:10.3390/sym13112097

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Despite these methods showing adequate performance in detail enhancement, they inevitably introduce colour distortion or amplify the inherent noise. Retinex-based methods [26,44,45] rely on the theory of colour constancy, and SSR, MSR, and MSRCR are typical representatives [46,47]. Among them, SSR can only enhance the grayscale image, MSR yields colour distortion in enhancement images, and MSRCR fails to highlight the image details.…”

Section: Introductionmentioning

confidence: 99%

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Liu

Lan

et al. 2023

IET Image Processing

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Lowlight images with low brightness and contrast, blurry details usually bring us an uncomfortable visual experience. To promote the quality of these deviation images, this paper presents a new and efficient approach, named MFMR, for enhancing lowlight images in the hue‐saturation‐value (HSV) colour space. Concretely, the multi‐angle filter is first applied to estimate the artifact‐free illumination and reflection component of the V‐channel. Afterward, the adaptive bi‐interval histogram with human visual characteristics and morphological operations is employed to process the former, adaptive gamma correction to process the latter for generating various feature maps. In the end, these feature maps are united via adaptive multi‐scale fusion strategy to reconstruct high‐quality images, which are characterized by high contrast and brightness, vivid colour, and clearer details. Extensive experiments show that this method is a well‐proven low‐light image enhancement approach, which outperforms the state‐of‐the‐art comparison methods. Furthermore, the proposed method also can yield satisfying images in the heavy foggy, yellow sand, underwater, and other severe conditions.

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Section: Introductionmentioning

confidence: 99%

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Liu

Lan

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

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Hybrid connected attentional lightweight network for gangue intelligent segmentation in top-coal caving face

et al. 2023

IFS

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The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset.

show abstract

Joint Dedusting and Enhancement of Top-Coal Caving Face via Single-Channel Retinex-Based Method with Frequency Domain Prior Information

Cited by 2 publications

References 37 publications

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Retinex‐inspired contrast stretch and detail boosting for lowlight image enhancement

Hybrid connected attentional lightweight network for gangue intelligent segmentation in top-coal caving face

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