An 𝓵p-nonconvex regularization method for image smoothing

Guoliang Zhu; Xiaoguang Lv; Xueman Sun; Biao Fang

doi:10.1117/12.2680655

27 June 2023 An 𝓵_p-nonconvex regularization method for image smoothing

Guoliang Zhu, Xiaoguang Lv, Xueman Sun, Biao Fang

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127050R (2023) https://doi.org/10.1117/12.2680655
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Image smoothing techniques are widely used in computer vision and graphics applications, such as detail enhancement, artifact removal, image denoising and high dynamic range (HDR) tone mapping. In this paper, an 𝓵_p-nonconvex minimization model is presented to achieve diverse smoothness of edges. To induce sparsity more strongly than the 𝓵₁ norm regularization, we take the nonconvex arctangent penalty function of the image gradient as the regularization term. To make the model more flexible and effective, we use the 𝓵_p norm function as the fidelity term. The majorization-minimization (MM) algorithm is employed for the proposed nonconvex optimization model. We discuss the convergence of the resulting MM algorithm. Comprehensive experiments and comparisons show that the proposed method is effective in a variety of image processing tasks.

Citation Download Citation

Guoliang Zhu, Xiaoguang Lv, Xueman Sun, and Biao Fang "An 𝓵_p-nonconvex regularization method for image smoothing", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127050R (27 June 2023); https://doi.org/10.1117/12.2680655

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KEYWORDS

Image enhancement

High dynamic range imaging

Image denoising

Image processing

Visualization

Image restoration

Image compression

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