Deep Constrained Least Squares for Blind Image Super-Resolution
This work addresses the problem of enhancing low-resolution images with unknown blur kernels for applications in computer vision, representing an incremental advance in blind super-resolution methods.
The paper tackles blind image super-resolution by reformulating the degradation model and introducing two novel modules for kernel estimation and restoration, achieving better accuracy and visual improvements on benchmarks like Gaussian8 and DIV2KRK.
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.