论文标题
迈向实时高清图像降雪：具有不对称编码器架构的高效金字塔网络
Towards Real-time High-Definition Image Snow Removal: Efficient Pyramid Network with Asymmetrical Encoder-decoder Architecture
论文作者
论文摘要
在冬季场景中，在雪下拍摄的图像的降解可能非常复杂，其中雪降解的空间分布因图像而异。最近的方法采用深度神经网络，直接从雪图像中恢复清洁的场景。但是，由于复杂的雪降解差异引起的悖论，实时实现可靠的高清图像是一个巨大的挑战。我们开发了一个新型的有效金字塔网络，具有非对称编码器架构，用于实时高清图像。我们提出的网络的一般思想是利用来自功能的多尺度特征流动流动流动。与以前最先进的方法相比，我们的方法实现了更好的复杂性 - 性能取舍，并有效地处理了高清和超高清图像的处理困难。 The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB on the SRRS test dataset.
In winter scenes, the degradation of images taken under snow can be pretty complex, where the spatial distribution of snowy degradation is varied from image to image. Recent methods adopt deep neural networks to directly recover clean scenes from snowy images. However, due to the paradox caused by the variation of complex snowy degradation, achieving reliable High-Definition image desnowing performance in real time is a considerable challenge. We develop a novel Efficient Pyramid Network with asymmetrical encoder-decoder architecture for real-time HD image desnowing. The general idea of our proposed network is to utilize the multi-scale feature flow fully and implicitly mine clean cues from features. Compared with previous state-of-the-art desnowing methods, our approach achieves a better complexity-performance trade-off and effectively handles the processing difficulties of HD and Ultra-HD images. The extensive experiments on three large-scale image desnowing datasets demonstrate that our method surpasses all state-of-the-art approaches by a large margin both quantitatively and qualitatively, boosting the PSNR metric from 31.76 dB to 34.10 dB on the CSD test dataset and from 28.29 dB to 30.87 dB on the SRRS test dataset.
