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Low-light Video Image Enhancement Based on Multiscale Retinex-like Algorithm

机译:基于多尺度Retinex的算法的低光视频图像增强

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The images taken in low-light environment always lose some details. Given that traditional multi-scale Retinex (MSR) algorithm always appears halo artifact in the edge area of the image, a multiscale Retinex-like is put forward for low-light video image enhancement. The proposed algorithm is operated in HSI color space. For Intensity layer, three Gaussian filters in traditional MSR algorithm are replaced by three guided filters to extract illumination component, then gets the reflection component, the sum of this two components as the new Intensity layer. For the Saturation layer, Gamma correction function is used to enhance Saturation, then the new HSI image is converted to RGB image. Four kinds of numerical methods are adopted to evaluate the images enhanced by different methods. The experimental results show that the proposed algorithm in terms of low-light image, not only makes the picture becomes clearer, but also extracts more image details. Furthermore the algorithm is more efficient.
机译:低光环境中拍摄的图像总是丢失一些细节。鉴于传统的多尺度视网膜(MSR)算法总是在图像的边缘区域中始终出现HALO伪影,提出了一种多尺度RETINEX-FERN,用于低光视频图像增强。所提出的算法在HSI颜色空间中运行。对于强度层,传统的MSR算法中的三个高斯滤波器由三个引导滤波器代替以提取照明组件,然后获取反射分量,这两个组件的总和作为新的强度层。对于饱和层,伽马校正函数用于增强饱和度,然后将新的HSI图像转换为RGB图像。采用四种数值方法来评估不同方法增强的图像。实验结果表明,该算法在低光图像方面,不仅使图片变得更加清晰,而且还提取更多图像细节。此外,算法更有效。

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