會議論文
| 學年 | 104 |
|---|---|
| 學期 | 1 |
| 發表日期 | 2015-11-18 |
| 作品名稱 | The analysis of reconstruction efficiency with compressive sensing in different k-spaces |
| 作品名稱（其他語言） | |
| 著者 | Chang, Feng-Cheng; Huang, Hsiang-Cheh |
| 作品所屬單位 | |
| 出版者 | |
| 會議名稱 | 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP) |
| 會議地點 | Kaohsiung, Taiwan |
| 摘要 | Compressive sensing is a potential technology for lossy image compression. With a given quality, we may represent an image with a few significant coefficients in the sparse domain. According to the sparse modeling theories, we may randomly sense a few number of measurements in a transform domain and later reconstruct the sparse representation. Typically the sensing domain is a low-complexity transform domain and the computation complexity lies on the reconstruction phase. In this paper, the linear and nonlinear compressive sensing approaches are briefly introduced. A few experiments are performed based on the nonlinear approach. Both 2D-DFT and 2D-DCT sensing domains are included to show their effects to the reconstruction quality. The simulation shows that the two domains produce comparable results if the proper comparison condition is considered. Some directions of revising the reconstruction process is also discussed in this paper. |
| 關鍵字 | Image reconstruction;Compressed sensing;Discrete Fourier transforms;Robot sensing systems;Redundancy |
| 語言 | en |
| 收錄於 | |
| 會議性質 | 國際 |
| 校內研討會地點 | 無 |
| 研討會時間 | 20151118~20151120 |
| 通訊作者 | |
| 國別 | TWN |
| 公開徵稿 | |
| 出版型式 | |
| 出處 | 2015 Third International Conference on Robot, Vision and Signal Processing (RVSP), pp. 67-70 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107500 ) |