  Image filters are fast, lightweight and effective, which make these
conventional wisdoms preferable as basic tools in vision tasks. In practical
scenarios, users have to tweak parameters multiple times to obtain satisfied
results. This inconvenience heavily discounts the efficiency and user
experience. We propose basis composition learning on single image filters to
automatically determine their optimal formulas. The feasibility is based on a
two-step strategy: first, we build a set of filtered basis (FB) consisting of
approximations under selected parameter configurations; second, a dual-branch
composition module is proposed to learn how the candidates in FB are combined
to better approximate the target image. Our method is simple yet effective in
practice; it renders filters to be user-friendly and benefits fundamental
low-level vision problems including denoising, deraining and texture removal.
Extensive experiments demonstrate that our method achieves an appropriate
balance among the performance, time complexity and memory efficiency.
