Super-resolution Using Constrained Deep Texture Synthesis
Hallucinating high frequency image details in single image super-resolution is a challenging task. Traditional super-resolution methods tend to produce oversmoothed output images due to the ambiguity in mapping between low and high resolution patches. We build on recent success in deep learning based texture synthesis and show that this rich feature space can facilitate successful transfer and synthesis of high frequency image details to improve the visual quality of super-resolution results on a wide variety of natural textures and images.
Code (0)
등록된 구현이 없습니다.
Tasks
Image Super-ResolutionSuper-ResolutionTexture SynthesisSimilar Papers 제목 키워드 기반
Graphcut Texture Synthesis for Single-Image Superresolution
Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture s…
FormTexture SynthesisEnhancing Texture Generation with High-Fidelity Using Advanced Texture Priors
The recent advancements in 2D generation technology have sparked a widespread discussion on using 2D priors for 3D shape and texture content generation. However, these methods often overlook the subsequent user operation…
Texture SynthesisFeature Representation Matters: End-to-End Learning for Reference-based Image Super-resolution
In this paper, we are aiming for a general reference-based super-resolution setting: it does not require the low-resolution image and the high-resolution reference image to be well aligned or with a similar texture. Inst…
Image GenerationImage Super-ResolutionReference-based Super-ResolutionSuper-ResolutionHigh resolution neural texture synthesis with long range constraints
The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis methods still struggle to reproduce large sc…
Texture SynthesisVocal Bursts Intensity PredictionImage Inpainting for High-Resolution Textures using CNN Texture Synthesis
Deep neural networks have been successfully applied to problems such as image segmentation, image super-resolution, coloration and image inpainting. In this work we propose the use of convolutional neural networks (CNN) …
Image InpaintingImage SegmentationImage Super-ResolutionSemantic Segmentation+3