An Edge Driven Wavelet Frame Model for Image Restoration
This is an incremental improvement for image processing applications, enhancing restoration quality in tasks like inpainting and deblurring.
The authors tackled image restoration by developing an edge-driven wavelet frame model that applies different regularization strengths to smooth and singular image regions, showing favorable performance in image inpainting and deblurring compared to existing models.
Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.