Using Channel Representations in Regularization Terms: A Case Study on Image Diffusion
Computer Vision and Pattern Recognition
Summary
The authors developed a new way to clean up noisy images by looking at different channels or features of the image instead of just pixels alone. They created a mathematical method that uses a soft-histogram to guide how the image smooths out over time, making the process more flexible and robust. Their technique works well to fix images with mixed types of noise, like blurry spots and missing pieces. When tested on normal black-and-white images, their method performed about as well as the best current cleaning techniques.
Authors
Christian Heinemann, Freddie Åström, George Baravdish, Kai Krajsek, Michael Felsberg, Hanno Scharr
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.