ABSTRACT: 针对海上舰船图像有效像素在整体像素中占比小的问题，提出一种基于目标检测网络的超分辨率方法。该方法包含两个阶段，结合bicubic变换，逐步地将图像的清晰度从粗到细地进行恢复。首先，第一阶段通过目标检测网络，检测出原图像中需要超分辨率的区域，然后，第二阶段将对应区域通过bicubic变换调整至指定分辨率，而后通过生成对抗网络增强图像细节。最终在自建数据集上的实验结果表明，与传统方法和现有基于深度神经网路的超分辨率重建算法相比，该算法不仅图像视觉效果最好，而且在数据集上的峰值信噪比(PSNR)平均提高了0.79 dB，结构相似性(SSIM)平均提高了0.04，证明了该算法的有效性。
Aiming at the problem that the effective pixels in the image of marine ships account for a small proportion in the total pixels, a super-resolution method based on target detection network is proposed. The method consists of two stages, combining with the bicubic transform, to restore the sharpness of the image from coarse to fine step by step. Firstly, in the first stage, super-resolution regions in the original image are detected through the target detection network. Then, in the se-cond stage, the corresponding regions are adjusted to the specified resolution by bicubic trans-formation, and then the image details are enhanced by generating the countermeasure network. Finally, the experimental results on the self-built dataset show that compared with the traditional method and the existing super-resolution reconstruction algorithm based on deep neural network, this algorithm not only has the best visual effect, but also improves the peak signal-to-noise ratio (PSNR) of the dataset by an average of 0.79 dB and the structural similarity (SSIM) by an average of 0.04, which proves the effectiveness of the algorithm.
扫一扫
1071
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
