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基于视觉传达优化的低清晰度船舶图像复原研究

Research on low definition ship image restoration based on visual communication optimization
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摘要 针对图像复原易出现复原图像颜色视觉特征失真问题,提出基于视觉传达优化的低清晰度船舶图像复原方法。该方法通过帧扫描方法,提取低清晰度船舶图像视觉传达信息的像素二维特征;使用基于最大后验概率的高分辨率船舶图像重组方法,将所提取低清晰度船舶图像视觉传达信息的像素二维特征,进行高分辨率特征重组,并引入基于视觉颜色模型的视觉传达效果优化复原方法,优化高分辨率特征重组后复原图像的颜色视觉传达效果。实验数据验证:该方法对低清晰度海域通行船舶监控图像复原处理后,图像视觉传达效果得到明显提升,且复原后船舶图像峰值信噪比、结构相似性指数接近1,图像特征失真小;复原后图像颜色特征显著性指数最大值达1.0,颜色特征细节显著性得以改善。 A low definition ship image restoration method based on visual communication optimization is studied to ad-dress the issue of color visual feature distortion in image restoration.This method extracts pixel two-dimensional features of visual communication information from low definition ship images through frame scanning method;Using a high-resolution ship image reconstruction method based on maximum a posteriori probability,the two-dimensional features of pixels extrac-ted from the visual communication information of low definition ship images are recombined into high-resolution features,and a visual communication optimization restoration method based on visual color models is introduced to optimize the col-or visual communication effect of the reconstructed image after the reconstruction of high-resolution features.Experimental data verification:After the restoration processing of low definition ship monitoring images in sea areas,the visual commu-nication effect of the images is significantly improved,and the peak signal-to-noise ratio and structural similarity index of the restored ship images are close to 1,with small image feature distortion;The maximum value of the color feature saliency in-dex of the restored image reaches 1.o,and the saliency of color feature details is improved.
作者 赵振华 ZHAO Zhen-hua(Henan Institute of Technology,Xinxiang 453000,China)
机构地区 河南工学院
出处 《舰船科学技术》 北大核心 2024年第5期65-68,共4页 Ship Science and Technology
关键词 视觉传达优化 低清晰度 船舶图像 复原方法 最大后验概率 视觉颜色模型 visual communication optimization low definition ship images restoration method maximum pos-terior probability visual Color Model
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