Deep Scale-Aware Image Smoothing

Abstract

Image smoothing, a technique for smoothing out insignificant textures while preserving meaningful structures, is an important component in many vision and graphics applications. Scale-awareness plays a fundamental role in image smoothing, as insignificant textures and noise usually are at fine scales while meaningful boundary objects are at coarse scales. This paper proposes a deep-learning-based scale-aware image smoothing method, which is built on a downscaling-upscaling mechanism with attention. The downscaling mechanism is for predicting large-scale salient structures, and the upscaling mechanism is for identifying and inferring insignificant small-scale details from the salient structures. In the experiments, the proposed one provides a noticeable performance improvement over recent methods.