Problem
Research questions and friction points this paper is trying to address.
non-linear diffusion
image reconstruction
Gaussian noise
impulse noise
channel representation
Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
🤖 AI Summary
本文提出了一种基于通道表示的非线性扩散滤波方法，通过新的能量函数解决图像重建问题，对混合噪声有良好表现。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Channel Representation
Non-linear Diffusion
Soft-histogram
Euler-Lagrange Equation
Image Reconstruction
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