OSBF: One-Sided Box Filter for Edge-Preserving Image Processing
Box filter is well-known for the image smoothing task, thanks to its effectiveness and computation efficiency. However, it can NOT preserve edges. In contrast, edge-preserving methods can NOT achieve the high computation performance as the box filter. To tackle this issue, in this paper, we present a one-sided box filter that can preserve edges much better than the box filter. Meanwhile, it has a similar high computation performance as the box filter. More specifically, we perform the box filter on nine one-sided local windows, and then select the most possible candidate as the result. Such selection imposes the non-linearity, which preserves the edges and corners. Several numerical experiments are conducted to confirm this edge-preserving property. At the same time, it has a similar computation performance as the box filter. It inherits the constant computation complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(1)$ </tex-math></inline-formula> and the linear complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(N)$ </tex-math></inline-formula> from the box filter with respect to the window size and the total number of pixels, respectively. We numerically confirm that this filter is the fastest method among the edge-preserving methods, including the classical and the state of the art approaches. It is at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10 \times $ </tex-math></inline-formula> faster than other edge-preserving methods. Thanks to the edge-preserving property and the high computation performance, the proposed one-sided box filter can be deployed in a large range of applications where the edge-preserving and high performance is required, such as real-time video processing, augmented reality and view synthesis.
Authors
- Yuanhao Gong (ORCID: https://orcid.org/0000-0001-5702-1927)
Institutions
- Shenzhen University (CN)
Publication Details
- Journal
- IEEE Access
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/access.2025.3555434
- Citations
- 6
- Primary Topic
- Image and Signal Denoising Methods
- Type
- article
- Field-Weighted Citation Impact
- 7.66
Funders
- National Natural Science Foundation of China
- Shenzhen Science and Technology Innovation Program