Arbitrary-scale super-resolution via deep learning: A comprehensive survey
Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …
the resolution of images or videos in computer vision. In recent years, significant progress …
Latent Modulated Function for Computational Optimal Continuous Image Representation
Z He, Z Jin - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Abstract The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural
Representation (INR) based works have achieved remarkable success in Arbitrary-Scale …
Representation (INR) based works have achieved remarkable success in Arbitrary-Scale …
Implicit neural feature fusion function for multispectral and hyperspectral image fusion
Multispectral and Hyperspectral Image Fusion (MHIF) is a practical task that aims to fuse a
high-resolution multispectral image (HR-MSI) and a low-resolution hyperspectral image (LR …
high-resolution multispectral image (HR-MSI) and a low-resolution hyperspectral image (LR …
Enhanced local distribution learning for real image super-resolution
Y Sun, Q Chen, W Xu, A Huang, C Yan… - Computer Vision and …, 2024 - Elsevier
Previous work has shown that CNN-based local distribution learning can efficiently
reconstruct high-resolution images, but with limited performance improvement against …
reconstruct high-resolution images, but with limited performance improvement against …
Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video
frames, potentially at various scaling factors, which presents several challenges regarding …
frames, potentially at various scaling factors, which presents several challenges regarding …
Learning Images Across Scales Using Adversarial Training
The real world exhibits rich structure and detail across many scales of observation. It is
difficult, however, to capture and represent a broad spectrum of scales using ordinary …
difficult, however, to capture and represent a broad spectrum of scales using ordinary …
Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution
Scale arbitrary super-resolution based on implicit image function gains increasing popularity
since it can better represent the visual world in a continuous manner. However, existing …
since it can better represent the visual world in a continuous manner. However, existing …
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
In an effort to improve the efficiency and scalability of single-image super-resolution (SISR)
applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into any …
applications, we introduce AnySR, to rebuild existing arbitrary-scale SR methods into any …
Memory-Efficient Discrete Cosine Transform Domain Weight Modulation Transformer for Arbitrary-Scale Super-Resolution
Recently, several arbitrary-scale models have been proposed for single-image super-
resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is …
resolution. Furthermore, the importance of arbitrary-scale single image super-resolution is …
LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation
Image resampling is a basic technique that is widely employed in daily applications, such as
camera photo editing. Recent deep neural networks (DNNs) have made impressive …
camera photo editing. Recent deep neural networks (DNNs) have made impressive …