Yinggan Tang

Quanwei Hu, Yinggan Tang, Xuguang Zhang

Image super-resolution (SR) in resource-constrained scenarios demands lightweight models balancing performance and latency. Convolutional neural networks (CNNs) offer low latency but lack non-local feature capture, while Transformers excel at non-local modeling yet suffer slow inference. To address this trade-off, we propose the Large Kernel Modulation Network (LKMN), a pure CNN-based model. LKMN has two core components: Enhanced Partial Large Kernel Block (EPLKB) and Cross-Gate Feed-Forward Network (CGFN). The EPLKB utilizes channel shuffle to boost inter-channel interaction, incorporates channel attention to focus on key information, and applies large kernel strip convolutions on partial channels for non-local feature extraction with reduced complexity. The CGFN dynamically adjusts discrepancies between input, local, and non-local features via a learnable scaling factor, then employs a cross-gate strategy to modulate and fuse these features, enhancing their complementarity. Extensive experiments demonstrate that our method outperforms existing state-of-the-art (SOTA) lightweight SR models while balancing quality and efficiency. Specifically, LKMN-L achieves 0.23 dB PSNR improvement over DAT-light on the Manga109 dataset at $\times$4 upscale, with nearly $\times$4.8 times faster. Codes are in the supplementary materials. The code is available at https://github.com/Supereeeee/LKMN.

Yinggan Tang, Quanwei Hu

The success of self-attention (SA) in Transformer demonstrates the importance of non-local information to image super-resolution (SR), but the huge computing power required makes it difficult to implement lightweight models. To solve this problem, we propose a pure convolutional neural network (CNN) model, LKFMixer, which utilizes large convolutional kernel to simulate the ability of self-attention to capture non-local features. Specifically, we increase the kernel size to 31 to obtain the larger receptive field as possible, and reduce the parameters and computations by coordinate decomposition. Meanwhile, a spatial feature modulation block (SFMB) is designed to enhance the focus of feature information on both spatial and channel dimension. In addition, by introducing feature selection block (FSB), the model can adaptively adjust the weights between local features and non-local features. Extensive experiments show that the proposed LKFMixer family outperform other state-of-the-art (SOTA) methods in terms of SR performance and reconstruction quality. In particular, compared with SwinIR-light on Manga109 dataset, LKFMixer-L achieves 0.6dB PSNR improvement at $\times$4 scale, while the inference speed is $\times$5 times faster. The code is available at https://github.com/Supereeeee/LKFMixer.

Bin Ren, Hang Guo, Lei Sun et al.

This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution (ESR). The challenge aimed to advance the development of deep models that optimize key computational metrics, i.e., runtime, parameters, and FLOPs, while achieving a PSNR of at least 26.90 dB on the $\operatorname{DIV2K\_LSDIR\_valid}$ dataset and 26.99 dB on the $\operatorname{DIV2K\_LSDIR\_test}$ dataset. A robust participation saw \textbf{244} registered entrants, with \textbf{43} teams submitting valid entries. This report meticulously analyzes these methods and results, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques. The analysis highlights innovative approaches and establishes benchmarks for future research in the field.