Abstract
It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradient-sparsity constraint, allowing details throughout the whole image to be removed automatically in a data-driven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal, and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications, including edge manipulation, image abstraction, detail magnification, and image resizing, show the applicability of the new method.
Highlights
Image smoothing is a fundamental and important issue in computer vision
After analyzing the properties of gradient maps in natural images, we find it is reasonable and practical to assume the ideal gradient map of a smoothed image should be sparse in space and nonlocally concentrating in amplitude
In order to achieve consistent smoothing performance, we propose a novel image smoothing method which we call the nonlocal gradient concentration (NGC) method
Summary
Image smoothing is a fundamental and important issue in computer vision Natural images contain both clear structural edges of objects and abundant details caused by lightness, textures, and so on. Image smoothing aims to produce images which discard insignificant details while preserving the main structural edges. Edge-preserving methods [2,3,4,5,6,7] consider that human eyes are sensitive to color changes between neighboring pixels, so they aim to preserve strong-contrast edges They cannot remove fine-scale details with large or oscillatory gradient amplitudes. Exploiting self-similarity in color and intensity space and in the transform domain, has led to many state-of-the-art algorithms for various applications [18] It has not been fully adopted in image smoothing.
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