Non-linear aggregation of filters to improve image denoising
arXiv:1904.00865v36 citations
Originality Incremental advance
AI Analysis
This addresses denoising for image processing applications, but appears incremental as it builds on existing filter aggregation techniques.
The paper tackled image denoising by introducing a non-linear aggregation method for filters, which significantly outperformed individual filters in numerical performance.
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.