From TV-L1 to Gated Recurrent Nets

Abstract

TV-L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is a classical diffusion-reaction model for low-level vision tasks, which can be solved by a duality based iterative algorithm. Considering the recent success of end-to-end learned representations, we propose a TV-LSTM network to unfold the duality based iterations into long short-term memory (LSTM) cells. To provide a trainable network, we relax the difference operators in the gate and cell update of TV-LSTM to trainable parameters. Then, the proposed end-to-end trainable TV-LSTMs can be naturally connected with various task-specific networks, e.g., optical flow estimation and image decomposition. Extensive experiments on optical flow estimation and structure + texture decomposition have demonstrated the effectiveness and efficiency of the proposed method.