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.
翻译:在冬季场景中,在雪下拍摄的图像的降解可能非常复杂,因为从图像到图像,积雪降解的空间分布各有不同。最近的方法采用深神经网络,直接从雪中的图像中恢复干净的场景。然而,由于复杂的雪降变异引起的矛盾现象,我们实现可靠的高定义图像实时脱落性能是一个相当大的挑战。我们开发了一个具有对称编码器脱色实时HD图像脱色的对称编码器脱色器结构的新型高效金字网。我们拟议网络的总体构想是,利用从地貌中完全和隐含的多尺度特征流来完全和隐含地清除地雷的线索。与以前最先进的脱尘方法相比,我们的方法实现了更好的复杂性交换,并有效地处理了HD和Ultra-HD图像的实时性能处理困难。关于三个大规模脱色图像数据集的广泛实验表明,我们的方法在定量和定性两方面都超过了所有最先进的方法,从31.76 dB至30.07 dB数据测试的PSNR测量数据从31.76 d.97到34.07 d.07 d.10 d.B。