Deep Flexible Structure Preserving Image Smoothing

Structure preserving image smoothing is fundamental to numerous multimedia, computer vision, and graphics tasks. This paper develops a deep network in the light of flexibility in controlling, structure preservation in smoothing, and efficiency. Following the principle of divide-and-rule, we decouple the original problem into two specific functionalities, i.e., controllable guidance prediction and image smoothing conditioned on the predicted guidance. Concretely, for flexibly adjusting the strength of smoothness, we customize a two-branch module equipped with a sluice mechanism, which enables altering the strength during inference in a fixed range from 0 (fully smoothing) to 1 (non-smoothing). Moreover, we build a UNet-in-UNet structure with carefully designed loss terms to seek visually pleasant smoothing results without paired data involved for training. As a consequence, our method can produce promising smoothing results with structures well-preserved at arbitrary levels through a compact model with 0.6M parameters, making it attractive for practical use. Quantitative and qualitative experiments are provided to reveal the efficacy of our design, and demonstrate its superiority over other competitors. The code can be found at https://github.com/lime-j/DeepFSPIS.