融合残差网络和纹理转移模型的单幅遥感图像超分辨率重建方法

Super-resolution reconstruction method of single remote sensing image based on residual network and texture transfer model

摘要:

遥感图像极易受到环境和天气因素的影响,分辨率降低,为提高单幅遥感图像细节信息的提取质量,在融合残差网络和纹理转移模型的支持下,优化设计单幅遥感图像超分辨率重建方法。考虑遥感图像的退化现象,按照遥感成像原理,获取低分辨率的单幅遥感图像。通过去雾、平滑滤波、颜色空间转换等步骤,实现对初始遥感图像的赋值。融合残差网络和纹理转移模型,进行单幅遥感图像纹理特征特征标记,确定图像重建规律,经过图像细节损失补偿,得出单幅遥感图像的超分辨率重建结果。以不同尺寸的遥感图像作为研究目标,实验结果表明,所提方法得出重建图像峰值信噪比提高约204,重建图像分辨率始终为1080dpi,同时图像重建任务的时间开销得到明显降低。

Abstract:

Remote sensing images are highly susceptible to environmental and weather factors, resulting in reduced resolution. In order to improve the quality of extracting detailed information from a single remote sensing image, a super-resolution reconstruction method for a single remote sensing image is optimized and designed with the support of fusion residual networks and texture transfer models. Considering the degradation phenomenon of remote sensing images, according to the principles of remote sensing imaging, obtain a single low resolution remote sensing image. By performing steps such as defogging, smoothing filtering, and color space conversion, the assignment of initial remote sensing images is achieved. By integrating residual networks and texture transfer models, single remote sensing image texture feature labeling is performed to determine the image reconstruction pattern. After compensating for image detail loss, the super-resolution reconstruction results of a single remote sensing image are obtained. Taking remote sensing images of different sizes as the research objective, the experimental results show that the proposed method improves the peak signal-to-noise ratio of the reconstructed image by about 204, and the resolution of the reconstructed image is always 1080dpi. At the same time, the time cost of the image reconstruction task is significantly reduced