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Bilateral regularized optimization model for edge-preserving image smoothing
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DOI：10.1016/j.imavis.2024.105031.png)
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Edge -preserving image smoothing is vital in the field of image processing and computational photography. The state-of-the-art filters based on optimization models have achieved promising performance. However, most of them fail to consider the spatial support in the regularization term, thus limiting the edge -preserving capabilities. In this paper, inspired by the bilateral filter, which consists of a range kernel and a spatial kernel. we propose to leverage bilateral kernel as a penalty function, and embed it into an optimization model for edge -preserving image smoothing. Furthermore, we propose to incorporate an edge -aware weighted scheme in the data term design, which further improves the edge -preserving capability. The bilateral function is non -convex and can be non -trivial to solve. In this paper, we propose a novel iterative solution based on fixed point iteration, where the main burden in each iteration is a bilateral filtering process. We have conducted extensive experiments to evaluate the proposed filter. Experiment results indicate that our filter benefits a variety of image processing tasks. Moreover, we propose an efficient approximation of the proposed filter, which is able to significantly accelerate the filtering process with neglectable sacrifice of smoothing quality.
Keyword:
Edge -preserving
Image smoothing
Bilateral regularization
Fixed point iteration
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