Removing Streak Interference from a Single Image based on Joint Priors

doi:10.23940/ijpe.18.11.p35.28972904

Abstract

Abstract: Streaks due to weather, such as rain or snow, degrade image quality and affect the performance of subsequential high-level vision tasks by the generated undesired artifacts. Hence, removing streak interference is an ongoing and challenging issue for many applications in real-time mobile surveillance systems. In this paper, streak interference removal from a single image is the focus. To sufficiently extract streak interference from an observed image, the image was firstly filtered with the nonsubsampled contourlet transform. Then, the residual part between the original and filtered image was decomposed into the streak component and detail component of background. Based on the additive layer model, we designed two specific priors that constrain the detail and streak interference respectively and established a model with joint priors for residual image decomposition. As a result, the resulting image can be synthesized with the filtered image and detail component. Experimental results show that our proposed method outperforms existing methods both qualitatively and quantitatively.

Key words: removing streak interference, nonsubsampled contourlet transform, sparse representation, Gaussian mixture model, prior

Ao Li, Xin Liu, Deyun Chen, Kezheng Lin, Guanglu Sun, and Qidi Wu. Removing Streak Interference from a Single Image based on Joint Priors [J]. Int J Performability Eng, 2018, 14(11): 2897-2904.

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