An Asynchronous Interactive Deraining Model and Its Applications in Multimedia
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Abstract
Rain removal plays an important role in many multimedia applications. Traditional algorithms tackle the deraining problem by the way of signal removal, which often leads to over-smoothness and unexpected artifacts. This paper attempts to solve this problem from a completely different perspective of signal decomposition, and introduces the interactions and constraints between the two decomposed signals during deraining procedure. Specifically, we propose an Asynchronous Interactive Generative Adversarial Network (AI-GAN) as a deraining model which progressively decomposes the rainy image into original and interfering parts through a two-branched structure. Each branch employs an asynchronous synthesis strategy for the corresponding generator, and interacts by exchanging feed-forward signal values and sharing corresponding feedback gradients to achieve the effect of adversarial optimization. The ‘adversarial’ we achieved here is not only the ‘adversarial’ between the generator and the discriminator, but also the ‘adversarial’ between the two generators, which has seldom been explored in existing GAN methods. Experimental results show that the proposed AI-GAN promises significant qualitative and quantitative improvement on the deraining task. Extensive experiments also validate the effectiveness of AI-GAN in many typical multimedia applications such as Video Coding, Action Recognition, Person Re-identification, and low-level image processing tasks, such as dehazing and denoising.
Paper
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