Adaptive Sampling for Computer Vision-Oriented Compressive Sensing

Author:

Liu Luyang1ORCID,Nishikawa Hiroki1ORCID,Zhou Jinjia2ORCID,Taniguchi Ittetsu1ORCID,Onoye Takao3ORCID

Affiliation:

1. Osaka University, JP

2. Hosei University, JP

3. Osaka University, Japan

Funder

JSPS KAKENHI Grant

Publisher

ACM

Reference17 articles.

1. Amir Adler , David Boublil , Michael Elad , and Michael Zibulevsky . 2016. A deep learning approach to block-based compressed sensing of images. arXiv preprint arXiv:1606.01519 ( 2016 ). Amir Adler, David Boublil, Michael Elad, and Michael Zibulevsky. 2016. A deep learning approach to block-based compressed sensing of images. arXiv preprint arXiv:1606.01519 (2016).

2. Ali Akbari , Diana Mandache , Maria Trocan , and Bertrand Granado . 2016 . Adaptive saliency-based compressive sensing image reconstruction . In 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 1–6. Ali Akbari, Diana Mandache, Maria Trocan, and Bertrand Granado. 2016. Adaptive saliency-based compressive sensing image reconstruction. In 2016 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 1–6.

3. Content-Aware Scalable Deep Compressed Sensing

4. Drone SAR Image Compression Based on Block Adaptive Compressive Sensing;Choi Jihoon;Remote Sensing,2021

5. Image Compressed Sensing Using Non-local Neural Network

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