Explicit Filterbank Learning for Neural Image Style Transfer and Image Processing.

Clicks: 264
ID: 85414
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #17 of 23 articles by views in ieee transactions on pattern analysis and machine intelligence

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Image style transfer is to re-render the content of one image with the style of another. Most existing methods couple content and style information in their network structures and hyper-parameters, and learn it as a black-box. For better understanding, this paper aims to provide a new explicit decoupled perspective. Specifically, we propose StyleBank, which is composed of multiple convolution filter banks and each filter bank explicitly represents one style. To transfer an image to a specific style, the corresponding filter bank is operated on the intermediate feature produced by a single auto-encoder. The StyleBank and the auto-encoder are jointly learnt in such a way that the auto-encoder does not encode any style information. This explicit representation also enables us to conduct incremental learning to add a new style and fuse styles at not only the image level, but also the region level. Our method is the first style transfer network that links back to traditional texton mapping methods, and provides new understanding on neural style transfer. We further apply this general filterbank learning idea to two different multi-parameter image processing tasks: edge-aware image smoothing and denoising. Experiments demonstrate that it can achieve comparable results to its single parameter setting counterparts.
Reference Key
chen2020explicitieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, Dongdong;Yuan, Lu;Liao, Jing;Yu, Nenghai;Hua, Gang;
Journal ieee transactions on pattern analysis and machine intelligence
Year 2020
DOI
10.1109/TPAMI.2020.2964205
URL
Keywords
adaptive non-local means (nlm) mr brain images median absolute deviation (mad) rician noise reduction

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.