用通道表示改进图像扩散滤波，有效处理混合噪声和缺失数据。
Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
- 基于通道编码的软直方图构建能量函数，导出非线性扩散更新方案。
- 在高斯与脉冲噪声混合场景下表现优异，对缺失数据有较强恢复能力。
- 适合图像重建与去噪任务，尤其适用于复杂噪声环境。
本文提出一种基于图像通道表示的新型非线性扩散滤波方法。通过通道编码获取像素邻域的软直方图表示，构建新的能量泛函，其对应的欧拉-拉格朗日方程导出具有额外加权项的鲁棒非线性扩散方案，能有效引导扩散过程。将该能量形式应用于图像重建问题，在高斯与脉冲型噪声混合（如缺失数据）条件下表现出色。在常见标量图像的去噪实验中，本方法性能优于其他扩散算法，并达到所考虑噪声类型下的先进水平。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。