Name
Title | ||
|---|---|---|
Tensor-Based Light Field Denoising By Exploiting Non-Local Similarities Across Multiple Resolutions |
Abstract | ||
|---|---|---|
Light field is a kind of 4D signal that contains rich information about position and angle of light, which can express the scene more accurately. Light field is easily affected by noise for the hardware sensitivity. This paper utilizes the intrinsic tensor sparsity model and integrates super-resolution(SR) into a unified light field denoising method based on tensor operation. Avoiding vectorization, we make full use of correlation of light field. By exploiting SR method, we avoid sub-pixel mis-alignment in the searching process of similar patch. Experimental results validate that our proposed method outperforms the state-of-art methods in terms of both objective and subjective quality on the HCI light field old dataset. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICIP40778.2020.9190646 | 2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | DocType | ISSN |
Light field, image denoising, tensor sparsity, super-resolution | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||