Image edges resolved well when using an overcomplete piecewise-polynomial model

2018/10/15 by Michaela Novosadová, Novosadová, Michaela, Pavel Rajmic +1
Computer Science · Engineering · Mathematics · #FOS: Mathematics #Image and Signal Denoising Methods #Medical Image Segmentation Techniques #Optimization and Control (math.OC) #Remote-Sensing Image Classification #Sparse and Compressive Sensing Techniques #math.OC

paper · pdf · doi:10.48550/arxiv.1810.06469

appears at the ICSPCS 2018 conference https://dspcs-witsp.com/icspcs_2018/

arxiv created 2018/10/15 · openalex publication_date 2018/10/15 · arxiv updated 2018/10/16 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28

Abstract

Used in the paper is an overcomplete piecewise-polynomial image model incorporating sparsity. The paper shows that using such a model, the edges in the image can be resolved robustly with respect to noise. Two variants of the proposed approach are both shown to be superior to the use of the classic edge detecting kernels. The proposed method is in turn also suitable for image segmentation.

Citations

• The group fused Lasso for multiple change-point detection
• Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)

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