In this paper, we tackle the problem of blind image super-resolution(SR) with
a reformulated degradation model and two novel modules. Following the common
practices of blind SR, our method proposes to improve both the kernel
estimation as well as the kernel-based high-resolution image restoration. To be
more specific, we first reformulate the degradation model such that the
deblurring kernel estimation can be transferred into the low-resolution space.
On top of this, we introduce a dynamic deep linear filter module. Instead of
learning a fixed kernel for all images, it can adaptively generate deblurring
kernel weights conditional on the input and yield a more robust kernel
estimation. Subsequently, a deep constrained least square filtering module is
applied to generate clean features based on the reformulation and estimated
kernel. The deblurred feature and the low input image feature are then fed into
a dual-path structured SR network and restore the final high-resolution result.
To evaluate our method, we further conduct evaluations on several benchmarks,
including Gaussian8 and DIV2KRK. Our experiments demonstrate that the proposed
method achieves better accuracy and visual improvements against
state-of-the-art methods.

本文提出一种盲图像超分辨率的新方法，使用重新构建的退化模型和两个新模块，通过对内核估计和内核基于高分辨率图像恢复的改进，实现更好的准确性和视觉改进，并在多个基准测试中进行了验证。