Deep raw image super-resolution. a NTIRE 2024 challenge survey
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge highlighting
the proposed solutions and results. New methods for RAW Super-Resolution could be …
the proposed solutions and results. New methods for RAW Super-Resolution could be …
Omni aggregation networks for lightweight image super-resolution
While lightweight ViT framework has made tremendous progress in image super-resolution,
its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme …
its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme …
Single image super-resolution: a comprehensive review and recent insight
H Al-Mekhlafi, S Liu - Frontiers of Computer Science, 2024 - Springer
Super-resolution (SR) is a long-standing problem in image processing and computer vision
and has attracted great attention from researchers over the decades. The main concept of …
and has attracted great attention from researchers over the decades. The main concept of …
Latticenet: Towards lightweight image super-resolution with lattice block
Deep neural networks with a massive number of layers have made a remarkable
breakthrough on single image super-resolution (SR), but sacrifice computation complexity …
breakthrough on single image super-resolution (SR), but sacrifice computation complexity …
Shufflemixer: An efficient convnet for image super-resolution
Lightweight and efficiency are critical drivers for the practical application of image super-
resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for …
resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for …
Effective pan-sharpening by multiscale invertible neural network and heterogeneous task distilling
As recognized, the ground-truth multispectral (MS) images possess the complementary
information (eg, high-frequency components) of low-resolution (LR) MS images, which can …
information (eg, high-frequency components) of low-resolution (LR) MS images, which can …
Learning with privileged information for efficient image super-resolution
Convolutional neural networks (CNNs) have allowed remarkable advances in single image
super-resolution (SISR) over the last decade. Most SR methods based on CNNs have …
super-resolution (SISR) over the last decade. Most SR methods based on CNNs have …
Addersr: Towards energy efficient image super-resolution
This paper studies the single image super-resolution problem using adder neural networks
(AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to …
(AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to …
Self-calibrated efficient transformer for lightweight super-resolution
Recently, deep learning has been successfully applied to the single-image super-resolution
(SISR) with remarkable performance. However, most existing methods focus on building a …
(SISR) with remarkable performance. However, most existing methods focus on building a …
Robust reference-based super-resolution via c2-matching
Abstract Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising
paradigm to enhance a low-resolution (LR) input image by introducing an additional high …
paradigm to enhance a low-resolution (LR) input image by introducing an additional high …