FreeTransformSR is a lightweight image super‑resolution network that uses a channel‑wise free low‑rank learnable transform to adaptively modulate features with minimal parameters. It adds a local feature modulation branch with depthwise convolution and a soft complexity adaptive module that fuses local convolution and window self‑attention based on texture characteristics. The model also employs an adaptive intensity modulation strategy and achieves competitive PSNR/SSIM on five benchmark datasets while using only 595K parameters and running faster than competing methods.
By Hongji Li, Yunhui Li
arXiv:2606.15158v3 Announce Type: replace
Abstract: Reference-guided generation (e.g., object compositing, customization) has progressed rapidly, yet current pipelines share a fundamental limitation:...
By Jeahun Sung, Dahyeon Kye, Soo Ye Kim, Jihyong Oh
arXiv:2608.30782v1 Announce Type: new
Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realis...
By Bingtian Qiao, Yue Shi, Yong Guo, Wenjun Zhang, Jiezhang Cao
arXiv:2609.16578v1 Announce Type: new
Abstract: The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing me...
By Hu Gao, Lizhuang Ma, Yulong Chen
arXiv:2406. 07435v2 Announce Type: replace-cross Abstract: Image restoration networks are usually comprised of an encoder and a decoder, responsible for aggregating image content from noisy, distorted data and to restore clean, undistorted images, respectively.
By Shashank Agnihotri, Julia Grabinski, Janis Keuper, Margret Keuper
Incorrect exposure degrades both image brightness and recoverable scene detail. Existing sRGB-based correction methods are limited by the non-linear image signal processing pipeline, whereas RAW-based...