SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)-Reference-Cited by-同舟云学术

SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)

Published:2023 Issue: Volume:11 Page:100256-100267
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Psychogyios Konstantinos¹^ORCID,Leligou Helen C.²^ORCID,Melissari Filisia¹^ORCID,Bourou Stavroula¹^ORCID,Anastasakis Zacharias¹^ORCID,Zahariadis Theodore¹^ORCID

Affiliation:

1. Synelixis Solutions S.A, Chalkida, Greece

2. Netcompany-Intrasoft S.A, Paiania, Greece

Funder

European Union’s Horizon Europe Research and Innovation Program

DAFNE+ Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10250775.pdf?arnumber=10250775

Reference44 articles.

1. Machine Learning in Medical Imaging

2. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

3. Cross-Resolution Distillation for Efficient 3D Medical Image Registration

4. Photorealistic Style Transfer via Wavelet Transforms

5. Deep Photo Style Transfer

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