arXiv Computer Vision
VibrantLeaves: A principled parametric image generator for training deep restoration models
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Machine Learning
Jun 18
Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
arXiv Machine Learning
Aug 6
A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise
arXiv:2608. 04944v1 Announce Type: cross Abstract: We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur.
arXiv AI
Jul 29
ScaleResfusion: Residual Rectified Flow based on Residual Vector Field
arXiv:2607. 25275v1 Announce Type: cross Abstract: Real-world Image Restoration (Real-IR) aims to recover high-quality (HQ) images from complex and unknown degradations.
arXiv Computer Vision
3d ago
Denoising Textures is a Different Ballgame Altogether!
The paper investigates how current image denoisers handle textures, finding that generic denoising models often over‑smooth fine patterns and that improving texture performance requires fine‑tuning on texture‑specific datasets. However, such fine‑tuning causes the models to lose their ability to denoise the original data, indicating a trade‑off. The authors argue that dedicated, texture‑aware denoisers are necessary for accurate texture reconstruction and for downstream tasks like generative modeling.
arXiv AI
Aug 11
Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance
arXiv:2608. 09482v1 Announce Type: cross Abstract: All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model.
arXiv Machine Learning
Jun 4
Efficient and Training-Free Single-Image Diffusion Models
arXiv:2606. 04299v1 Announce Type: cross Abstract: We consider the problem of generating images whose internal structure -- defined by the distribution of patches across multiple scales -- matches that of a single reference image.