Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional learned prior to the inference phase of a flow-based SR model. This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image. Our framework is designed to seamlessly integrate with any contemporary flow-based SR model without modifying its architecture or pre-trained weights. We evaluate the effectiveness of our proposed framework through extensive experiments and ablation analyses. The proposed framework successfully addresses all the inherent issues in flow-based SR models and enhances their performance in various SR scenarios. Our code is available at: https://github.com/liyuantsao/BFSR

Existing image restoration approaches typically employ extensive networks specifically trained for designated degradations. Despite being effective, such methods inevitably entail considerable storage costs and computational overheads due to the reliance on task-specific networks. In this work, we go beyond this well-established framework and exploit the inherent commonalities among image restoration tasks. The primary objective is to identify components that are shareable across restoration tasks and augment the shared components with modules specifically trained for individual tasks. Towards this goal, we propose AdaIR, a novel framework that enables low storage cost and efficient training without sacrificing performance. Specifically, a generic restoration network is first constructed through self-supervised pre-training using synthetic degradations. Subsequent to the pre-training phase, adapters are trained to adapt the pre-trained network to specific degradations. AdaIR requires solely the training of lightweight, task-specific modules, ensuring a more efficient storage and training regimen. We have conducted extensive experiments to validate the effectiveness of AdaIR and analyze the influence of the pre-training strategy on discovering shareable components. Extensive experimental results show that AdaIR achieves outstanding results on multi-task restoration while utilizing significantly fewer parameters (1.9 MB) and less training time (7 hours) for each restoration task. The source codes and trained models will be released.

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks.