  Recent Blind Image Super-Resolution (BSR) methods have shown proficiency in
general images. However, we find that the efficacy of recent methods obviously
diminishes when employed on image data with blur, while image data with
intentional blur constitute a substantial proportion of general data. To
further investigate and address this issue, we developed a new super-resolution
dataset specifically tailored for blur images, named the Real-world Blur-kept
Super-Resolution (ReBlurSR) dataset, which consists of nearly 3000 defocus and
motion blur image samples with diverse blur sizes and varying blur intensities.
Furthermore, we propose a new BSR framework for blur images called
Perceptual-Blur-adaptive Super-Resolution (PBaSR), which comprises two main
modules: the Cross Disentanglement Module (CDM) and the Cross Fusion Module
(CFM). The CDM utilizes a dual-branch parallelism to isolate conflicting blur
and general data during optimization. The CFM fuses the well-optimized prior
from these distinct domains cost-effectively and efficiently based on model
interpolation. By integrating these two modules, PBaSR achieves commendable
performance on both general and blur data without any additional inference and
deployment cost and is generalizable across multiple model architectures. Rich
experiments show that PBaSR achieves state-of-the-art performance across
various metrics without incurring extra inference costs. Within the widely
adopted LPIPS metrics, PBaSR achieves an improvement range of approximately
0.02-0.10 with diverse anchor methods and blur types, across both the ReBlurSR
and multiple common general BSR benchmarks. Code here:
https://github.com/Imalne/PBaSR.
