FIND THE WAY BACK: INVERTIBLE KERNEL ESTIMATOR FOR BLIND IMAGE SUPER-RESOLUTION

We address the task of zero-shot blind image super-resolution, where it aims to recover the high-resolution details from the low-resolution input image under a challenging problem setting of having no external training data, no prior assumption on the downsampling kernel, and no pre-training components used for estimating the downsampling kernel. While existing zero-shot blind super-resolution works follow the strategy of firstly estimating the downsampling kernel via cross-scale recurrence and then learning the non-blind upsampling model, we in turn propose a carefully-designed invertible network for modeling both the downsampling and upsampling operations at once. Specifically, the invertible property enables the use of cross-scale recurrence across more scales and thus further benefits the overall model training. We conduct extensive experiments to demonstrate our proposed method's superior performance over several baselines and its effectiveness in handling the images downsampled by nonlinear kernels.