LLSURE: Local Linear SURE-Based Edge- Preserving Image Filtering
Abstract
In this LLSURE: Local Linear SURE-Based Edge- Preserving Image Filtering paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein’s unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.
Conclusion
LLSURE: Local Linear SURE-Based Edge- Preserving Image Filtering,We have presented a new approach for performing high-quality edge-preserving image filtering in real time. Our approach is based on a local linear model and uses the principle of Stein’s unbiased risk estimate (SURE) to determine the optimal affine transform coefficients.
The proposed approach has several desirable features. First, since using a local linear model, it is simple and can be compute efficiently with arbitrary kernel sizes in constant time. Second, it has the nice property of edge-preserving smoothing which can removed noise while preserve fine details and geometrical structures in the original image.
