Self-Supervised Multi-Scale Network for Blind Image Deblurring via Alternating Optimization
This addresses the problem of blind image deblurring for computer vision applications, presenting an incremental improvement by combining self-supervised learning with multi-scale estimation to reduce computational complexity.
The paper tackles blind image deblurring by proposing a self-supervised multi-scale method that jointly estimates the latent image and blur kernel via alternating optimization, achieving superior performance on synthetic and realistic datasets, particularly for large and real-world blurs.
Blind image deblurring is a challenging low-level vision task that involves estimating the unblurred image when the blur kernel is unknown. In this paper, we present a self-supervised multi-scale blind image deblurring method to jointly estimate the latent image and the blur kernel via alternating optimization. In the image estimation step, we construct a multi-scale generator network with multiple inputs and multiple outputs to collaboratively estimate latent images at various scales, supervised by an image pyramid constructed from only the blurred image. This generator places architectural constraints on the network and avoids the need for mathematical expression of image priors. In the blur kernel estimation step, the blur kernel at each scale is independently estimated with a direct solution to a quadratic regularized least-squares model for its flexible adaptation to the proposed multi-scale generator for image estimation. Thanks to the collaborative estimation across multiple scales, our method avoids the computationally intensive coarse-to-fine propagation and additional image deblurring processes used in traditional mathematical optimization-based methods. Quantitative and qualitative experimental results on synthetic and realistic datasets demonstrate the superior performance of our method, especially for handling large and real-world blurs.