Decoupled Frequency Learning for Dynamic Scene Deblurring

Despite end-to-end deep learning methods have recently advanced state-of-the-art for dynamic scene deblurring, they are often biased towards learning low-frequency (LF) information, thus missing sufficient high-frequency (HF) details. In this paper, we experimentally verify that different image frequencies affect the final deblurring quality in different manners. Considering this, we point out that the LF learning bias problem arises from the existing training scheme with frequencies coupled, to some extent. Concretely, current training scheme fails to distinguish different frequencies but optimize them as a whole towards one common objective, thereby resulting in sub-optimal results. To ameliorate this problem, we propose an alternative training strategy, namely Decoupled Frequency Learning (DFL). Specifically, DFL treats deblurring task as two separate sub-tasks, which correspond to image LF and HF components, respectively. Different losses are tailored-designed for different frequencies to better guide their learning towards appropriate objectives. The proposed DFL scheme is simple yet effective, and compatible to any existing deep models. Extensive experiments on public benchmarks demonstrate its clear benefits to the state-of-the-art in terms of both quantitative measures and perceptual quality.