DVDR-SRGAN:差值密集的剩余超级分辨率生成对抗网络
Hang Qu1, Huawei Yi1, Yanlan Shi1
1School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的差异值密度剩余网络 (DVDR-SRGAN),用于单图像超分辨率. 该模型通过专注于关键区域来增强图像重建,提高视觉质量和细节准确性.
科学领域:
- 计算机视觉 计算机视觉
- 图像处理 图像处理
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 生成对抗网络 (GAN) 擅长在超高分辨率中生成人眼愉快的图像纹理.
- 然而,GAN经常引入文物,虚假的纹理,与地面真相图像相比,细节上的差异.
- 现有的方法很难完全捕捉和准确地重建精细的图像细节.
研究的目的:
- 在单图像超分辨率重建中提高视觉质量.
- 为解决基于GAN的超分辨率中常见的文物和细节偏差.
- 增强相邻网络层之间的特征相关性分析.
主要方法:
- 提出了一个差异值密度剩余网络 (DVDR-SRGAN),专注于特征相关性.
- 用于特征扩展的解卷,然后用于特征提取的卷积.
- 计算了提取前和提取后特征之间的差值,以突出需要注意的区域.
- 利用密集的剩余连接来实现完整的特征放大和准确的差异值提取.
- 引入了关节损失功能,以融合高频和低频信息,以改善视觉效果.
主要成果:
- DVDR-SRGAN模型在标准数据集 (Set5,Set14,BSD100,Urban) 上显示出更好的性能.
- 在峰值信号与噪声比率 (PSNR),结构相似度指数 (SSIM) 和学习感知图像补丁相似度 (LPIPS) 度量方面取得了卓越的结果.
- 超越了包括Bicubic,SRGAN,ESRGAN,Beby-GAN和SPSR在内的已建立的模型.
结论:
- 拟议的DVDR-SRGAN有效地减轻了文物,并在单图像超分辨率中增强了细节重建.
- 特性相关性分析和差异值提取对于提高超分辨率精度至关重要.
- 关节损失函数通过整合多频信息来进一步完善视觉质量.
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