Abstract
Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.
Highlights
The quality of video captured from outdoor electronic equipments can be heavily degraded by bad weather such as rain, snow, haze or fog
The displaced frame difference (DFD) between neighboring frames is used as the measure of temporal discontinuity in the five frame window
Temporal and spatial constraints are unified into a maximum a posteriori (MAP) computation
Summary
The quality of video captured from outdoor electronic equipments can be heavily degraded by bad weather such as rain, snow, haze or fog. In previous reported image-based methods, the physical property and image spatial-temporal characters of rain were applied efficiently. The goal of spatio-based method is try to remove image high frequency information containing rain steaks. To some extent, this is similar to some image denoise technique. Such as [8,12,15], neighboring frames are incorporated into the whole detection framework according to the characteristics of rain steaks in temporal field Both spatial and temporal methods rely on image/video spatial and temporal redundancy. In order to characterize rain detection, we try to harmonize the spatial and temporal considerations into our new Bayesian framework to make full use of the image/video redundant information.
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