发表机构
Forschungszentrum Jülich; Linköping University（于利希研究中心; 林雪平大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于图像通道表示的新型非线性扩散滤波方法，构建含通道表示权重项的能量泛函，在含混合噪声的图像重建与去噪任务中表现具竞争力。
AI 中文摘要
本研究提出一种基于图像通道表示的新型非线性扩散滤波方法。为推导扩散更新方案，我们利用通道编码得到的图像像素邻域的软直方图表示构建新型能量泛函，其对应的欧拉-拉格朗日方程产生带附加权重项的非线性鲁棒扩散方案，该权重项源自通道表示以引导扩散过程。我们将该能量公式应用于图像重建问题，在存在高斯噪声与脉冲类噪声（如数据缺失）混合的场景中展现良好性能；在常见标量值图像的去噪实验中，针对所考虑的噪声类型，我们的方法与其他扩散方案及最先进的去噪方法相比表现具有竞争力。
英文摘要
In this work we propose a novel non-linear diffusion filtering approach for images based on their channel representation. To derive the diffusion update scheme we formulate a novel energy functional using a soft-histogram representation of image pixel neighborhoods obtained from the channel encoding. The resulting Euler-Lagrange equation yields a non-linear robust diffusion scheme with additional weighting terms stemming from the channel representation which steer the diffusion process. We apply this novel energy formulation to image reconstruction problems, showing good performance in the presence of mixtures of Gaussian and impulse-like noise, e.g. missing data. In denoising experiments of common scalar-valued images our approach performs competitive compared to other diffusion schemes as well as state-of-the-art denoising methods for the considered noise types.
Journal refProceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP 2014), vol. 2, pp. 48-55, SciTePress, 2014
