Nonlinear Spectral Image Fusion
원문 초록
In this paper we demonstrate that the framework of nonlinear spectral decompositions based on total variation (TV) regularization is very well suited for image fusion as well as more general image manipulation tasks. The well-localized and edge-preserving spectral TV decomposition allows to select frequencies of a certain image to transfer particular features, such as wrinkles in a face, from one image to another. We illustrate the effectiveness of the proposed approach in several numerical experiments, including a comparison to the competing techniques of Poisson image editing, linear osmosis, wavelet fusion and Laplacian pyramid fusion. We conclude that the proposed spectral TV image decomposition framework is a valuable tool for semi- and fully-automatic image editing and fusion.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Visual Agentic Reinforcement Fine-Tuning
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
Visual Text Processing: A Comprehensive Review and Unified Evaluation
Step1X-Edit: A Practical Framework for General Image Editing
LLM-guided Instance-level Image Manipulation with Diffusion U-Net Cross-Attention Maps
Can We Get Rid of Handcrafted Feature Extractors? SparseViT: Nonsemantics-Centered, Parameter-Efficient Image Manipulation Localization through Spare-Coding Transformer
토론
이 논문과 연결된 포럼 글이 아직 없습니다.