Cross-Scale Self-Supervised Blind Image Deblurring via Implicit Neural Representation

Tianjing Zhang, Yuhui Quan, Hui Ji

Keywords: Blind Image Deblurring, Self-Supervised Learning, Implicit Neural Representation

Abstract: Blind image deblurring (BID) is an important yet challenging image recovery problem. Most existing deep learning methods require supervised training with ground truth (GT) images. This paper introduces a self-supervised method for BID that does not require GT images. The key challenge is to regularize the training to prevent over-fitting due to the absence of GT images. By leveraging an exact relationship among the blurred image, latent image, and blur kernel across consecutive scales, we propose an effective cross-scale consistency loss. This is implemented by representing the image and kernel with implicit neural representations (INRs), whose resolution-free property enables consistent yet efficient computation for network training across multiple scales. Combined with a progressively coarse-to-fine training scheme, the proposed method significantly outperforms existing self-supervised methods in extensive experiments.

Primary Area: Machine vision

Submission Number: 4500