Kavli Affiliate: Li Xin Li
| First 5 Authors: Qian Ning, Weisheng Dong, Guangming Shi, Leida Li, Xin Li
| Summary:
Deep neural networks (DNNs) based methods have achieved great success in
single image super-resolution (SISR). However, existing state-of-the-art SISR
techniques are designed like black boxes lacking transparency and
interpretability. Moreover, the improvement in visual quality is often at the
price of increased model complexity due to black-box design. In this paper, we
present and advocate an explainable approach toward SISR named model-guided
deep unfolding network (MoG-DUN). Targeting at breaking the coherence barrier,
we opt to work with a well-established image prior named nonlocal
auto-regressive model and use it to guide our DNN design. By integrating deep
denoising and nonlocal regularization as trainable modules within a deep
learning framework, we can unfold the iterative process of model-based SISR
into a multi-stage concatenation of building blocks with three interconnected
modules (denoising, nonlocal-AR, and reconstruction). The design of all three
modules leverages the latest advances including dense/skip connections as well
as fast nonlocal implementation. In addition to explainability, MoG-DUN is
accurate (producing fewer aliasing artifacts), computationally efficient (with
reduced model parameters), and versatile (capable of handling multiple
degradations). The superiority of the proposed MoG-DUN method to existing
state-of-the-art image SR methods including RCAN, SRMDNF, and SRFBN is
substantiated by extensive experiments on several popular datasets and various
degradation scenarios.
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