An effective deep network using target vector update modules for image restoration

Abstract Image restoration (IR) has been widely used in many computer vision applications. The model-based IR methods have clear theoretical bases. However, numerous hyper-parameters need to be set empirically, which is often challenging and time-consuming. Because of the powerful nonlinear fitting ability, deep convolutional neural networks (CNNs) have been widely used in IR tasks in recent years. However, it is challenging to design new network architecture to further significantly improve the IR performance. Inspired by the plug and play (P&P) methods, we first decouple the original IR problem into two subproblems with the variable splitting technique. Then, derived from the model-based methods, a novel deep CNN framework in the transformation domain is proposed to mimic the optimization process of the two subproblems. The proposed framework is driven effectively by the target vector update (TVU) module. Extensive experiments demonstrate the effectiveness of our proposed method over other state-of-the-art IR methods.