TL;DR
A Cascaded Multi-Scale Feature Modulation (CMSFM) module, which progressively aggregates multi-scale contextual representations through cascaded feature modulation, and a Multi-Scale Variance-Guided Attention (MSVGA) module, which exploits feature variance as a lightweight statistical cue to capture the magnitude of local feature fluctuations and provide complementary guidance for adaptive feature modulation.
Image super-resolution (SR), which aims to reconstruct a high-resolution image from a low-resolution input, has progressed from convolutional neural networks (CNNs) to transformer-based architectures. Despite this progress, lightweight transformer SR remains challenging: local or window-based operations provide limited...
This work proposes a lightweight dual-domain attention aggregation network (LDANet), aiming to achieve image super-resolution with both high efficiency and high quality, and proposes the pixel-embedding channel attention module, which achieves cross-channel global context awareness by jointly modeling pixel-level spati...
Remote sensing image super-resolution (SR) aims to recover high-resolution images from low-resolution observations, but existing deep networks often emphasize spatial-domain modeling and pixel-wise optimization, which may limit their ability to restore high-frequency textures and preserve perceptual structures. To addr...
Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightweight super-resolution network based on a channel-wise free low-rank learnable transform. The transform learns task-adaptive basis functions in a data-driven manner, en...
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