Controllable Universal Edge-Preserving Image Filtering
Shijun Liang, Dongdong Fu · 2024
In this study, we investigate the Deep Image Prior (DIP) in enhancing image smoothing, a crucial component in numerous computer vision and graphics applications. Although deep learning has demonstrated remarkable achievements in these domains, it often falls short in flexibility and controllability, in contrast to traditional methods, which are more adaptable and typically exhibit subpar performance. Notably, some end-to-end deep learning models offer control over edge preservation, yet their performance remains marginally suboptimal. To address this shortcoming, we introduce an innovative network architecture that diverges from the traditional U-Net model, featuring a Laplacian pyramid as the encoder and a deep decoder as the decoding component, integrated with a bilateral filter loss to improve DIP. This design aids the network in rapidly assimilating essential low-frequency information. Our approach excels in retaining texture details, significantly improving image smoothing and related tasks beyond the capabilities of standard DIP methods. Moreover, our technique outperforms the leading unsupervised method, pyramid texture filtering, in texture filtering tasks and other applications.