云南大学信息学院;
为了有效解决水下图像亮度、对比度过低和颜色混乱等问题,提出一种改进的多尺度密集残差网络的水下图像增强方法。对原始图像进行多尺度特征提取,更好地保留了图像细节,通过改进的密集残差网络对水下图像进行增强处理,提升图像亮度和对比度,校正图像颜色,在每个密集残差网络间添加了SK注意力机制,可以选择性地捕捉输入图像的关键信息并进行处理,将增强后的水下图像进行多尺度融合。通过Type和EUVP两个水下图像数据集对所提出方法进行验证,基于物理模型和数据驱动的6种方法进行了主观效果和客观指标间的比较。在主观效果的定性分析中发现,所提出的方法在提高亮度和对比度方面取得了很大的进步。在客观图像评价指标的定量分析中,峰值信噪比(Peak Signal to Noise Ratio, PSNR)、结构相似性(Structural Similarity, SSIM)、信噪比(Signal to Noise Ratio, SNR)、均方误差(Mean Square Error, MSE)、视觉信息保真度(Visual Information Fidelity, VIF)、信息保真度准则(Information Fidelity Criterion, IFC)、噪声质量评价(Noise Quality Measure, NQM)、亮度顺序误差(Lightness Order Error, LOE)和自然图像质量评价(Natural Image Quality Evaluator, NIQE)指标较现有的水下图像增强算法分别提高了1.5%,1%,1.2%,1.2%,1.3%,1.2%,1.7%,3.0%和1.1%。提出的改进多尺度密集残差网络不仅可以增强图像的亮度、对比度以及校正图像的颜色,而且可以应用于更广泛的水域场景。
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| 下载次数 | 被引频次 | 阅读次数 |
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基本信息:
DOI：
中图分类号:TP391.41;TP183
引用信息:
[1]卫依雪,周冬明,王长城等.采用多尺度密集残差网络的水下图像增强[J].无线电工程,2021,51(09):870-878.
基金信息:
国家自然科学基金资助项目(62066047,61365001,61463052)~~