Fan Zhang

Fan Zhang, Shaodi You, Yu Li et al.

Rain is transparent, which reflects and refracts light in the scene to the camera. In outdoor vision, rain, especially rain streaks degrade visibility and therefore need to be removed. In existing rain streak removal datasets, although density, scale, direction and intensity have been considered, transparency is not fully taken into account. This problem is particularly serious in night scenes, where the appearance of rain largely depends on the interaction with scene illuminations and changes drastically on different positions within the image. This is problematic, because unrealistic dataset causes serious domain bias. In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset. Unlike existing datasets, by using 3D computer graphic platform (namely GTA V), we are allowed to infer the three dimensional interaction between rain and illuminations, which insures the photometric realness. Current release of the dataset contains 12,860 HD rainy images and 1,286 corresponding HD ground truth images in diversified night scenes. A systematic benchmark and analysis are provided along with the dataset to inspire further research.

Wei Hu, Wei Li, Fan Zhang et al.

Decolorization is the process to convert a color image or video to its grayscale version, and it has received great attention in recent years. An ideal decolorization algorithm should preserve the original color contrast as much as possible. Meanwhile, it should provide the final decolorized result as fast as possible. However, most of the current methods are suffering from either unsatisfied color information preservation or high computational cost, limiting their application value. In this paper, a simple but effective technique is proposed for real-time decolorization. Based on the typical rgb2gray() color conversion model, which produces a grayscale image by linearly combining R, G, and B channels, we propose a dominant color hypothesis and a corresponding distance measurement metric to evaluate the quality of grayscale conversion. The local optimum scheme provides several "good" candidates in a confidence interval, from which the "best" result can be extracted. Experimental results demonstrate that remarkable simplicity of the proposed method facilitates the process of high resolution images and videos in real-time using a common CPU.