An efficient blur kernel estimation method for blind image Super-Resolution

Abstract

Many existing space-variant blind image Super-Resolution (SR) methods require the generation of blur kernels for all input pixels. However, the existing methods overlook the possibility of similar blur kernel patterns among adjacent pixels, leading to spatial incoherence and wasteful computational resources. In this study, we introduce an efficient two-stage method for estimating blur kernels. Instead of generating pixel-wise kernels, a limited set of kernels at pixels with distinct information are generated first and the remaining kernels are reconstructed based on this initial set. This novel method, referred to as Anchor Detection and Kernel Reconstruction, comprises two main components: an Anchor Detection Module (ADM) and a Kernel Reconstruction Module (KRM). The objective of ADM is to identify pixels in a given Low-Resolution image that contain rich information, which are called anchors. The corresponding blur kernels, denoted as anchor kernels, are then generated for these identified pixels using a complete backbone network. The remaining blur kernels are reconstructed using KRM with a lightweight interpolation method based on the anchor kernels to enhance spatial consistency among the reconstructed pixels. Extensive experiments demonstrate that the proposed ADKR method maintains comparable performance while estimating only 50% of blur kernels with a full backbone network, reaching approximately 20% reduction in FLOPs. The code has been made available at https://github.com/xuyimin0926/ADKR.