LDP: Language-Driven Dual-Pixel Image Defocus Deblurring Network

Abstract

Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task. Existing blur map-based deblurring methods have demonstrated promising results. In this paper we propose to the best of our knowledge the first framework to introduce the contrastive language-image pre-training framework (CLIP) to achieve accurate blur map estimation from DP pairs unsupervisedly. To this end we first carefully design text prompts to enable CLIP to understand blur-related geometric prior knowledge from the DP pair. Then we propose a format to input stereo DP pair to the CLIP without any fine-tuning where the CLIP is pre-trained on monocular images. Given the estimated blur map we introduce a blur-prior attention block a blur-weighting loss and a blur-aware loss to recover the all-in-focus image. Our method achieves state-of-the-art performance in extensive experiments (see Fig. 1).