Image Reconstruction using Superpixel Clustering and Tensor Completion
Not on the map. Why?
Abstract (arXiv)
This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the representative pixel and find that the centroid pixel performs the best. We also propose two smooth tensor completion algorithms that can effectively reconstruct different types of images from the selected pixels. Our experiments show that our superpixel-based method achieves better results than uniform sampling for various missing ratios.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 68 references that are in the map. First posted on arXiv 16 May 2023.
Builds on
16 of 68 references.
References · 16
- 2020Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotationcited by 128
- 2018Tensor Robust Principal Component Analysis with A New Tensor Nuclear Normcited by 110
- 2015Exact tensor completion using t-SVDcited by 70
- 2017Tensor Completion Algorithms in Big Data Analyticscited by 48
- 2017Efficient Low Rank Tensor Ring Completioncited by 45
- 2019Low-Rank Matrix Completion: A Contemporary Surveycited by 43
- 2018Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completioncited by 42
- 2015Smooth PARAFAC Decomposition for Tensor Completioncited by 34
- 2016Tensor Methods and Recommender Systemscited by 24
- 2019Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scenecited by 22
- 2018Tensor N-tubal rank and its convex relaxation for low-rank tensor recoverycited by 19
- 2021Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Imagescited by 18
4 more
- 2018Missing Slice Recovery for Tensors Using a Low-rank Model in Embedded Spacecited by 17
- 2018Fast and Accurate Tensor Completion with Total Variation Regularized Tensor Trainscited by 14
- 2017Low-Rank Tensor Completion by Truncated Nuclear Norm Regularizationcited by 7
- 2023Nonnegative Low-Rank Tensor Completion via Dual Formulation with Applications to Image and Video Completioncited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.