KERNEL ESTIMATION NETWORK FOR BLIND SUPER-RESOLUTION

Existing super-resolution (SR) methods commonly assume that the degradation kernels are fixed and known (e.g., bicubic downsampling or single Gaussian blurring kernel). However, these methods suffer a severe performance drop when the real degradations deviate from this assumption. To address this issue, this paper proposes a novel kernel estimation network (KENet) for kernel prediction. Specifically, KENet predicts the degradation kernels by optimizing the kernel space loss in a supervised way, without extra iterations at the inference time. Moreover, we introduce an adaptive attention loss to constrain the kernel optimization space, which can bias the allocation of trainable model parameters towards the most informative components of the estimation kernels. Extensive experiments on synthetic and real images show that the proposed KENet not only encourages a more accurate way to predict degradation kernels but also outperforms existing state-of-the-art blind SR methods when combined with non-blind SR methods.