Lightweight Multi‑Information Fusion Network Sets New Benchmark for Image Deblurring

Global: Light‑weight Multi‑Information Fusion Network Sets New Benchmark for Image Deblurring

A team of computer‑vision researchers released an updated version of their image‑deblurring model on Jan. 27, 2026, extending work originally submitted on Jan. 14, 2021. The paper, authored by Yanni Zhang, Yiming Liu, Qiang Li, Miao Qi, Dahong Xu, Jun Kong and Jianzhong Wang, introduces a lightweight multi‑information fusion network (LMFN) that aims to reduce computational load while preserving high‑quality restoration.

Background

Image deblurring is a critical preprocessing step for photography, surveillance and autonomous‑driving systems. Conventional deep‑learning approaches often rely on large parameter counts, which can impede deployment on resource‑constrained hardware.

Methodology

The proposed LMFN follows an encoder‑decoder paradigm. During encoding, image features are projected into several reduced‑scale spaces to capture multi‑scale information without substantial loss. In the decoding phase, a distillation network leverages residual learning to keep the model lightweight. An attention‑based information‑fusion strategy further integrates distilled features across channels, enhancing the network’s ability to recover fine details.

Results

According to the authors’ abstract, the LMFN achieves state‑of‑the‑art deblurring performance while using fewer parameters than competing models. The network reportedly outperforms existing methods in terms of model complexity, suggesting a favorable trade‑off between accuracy and efficiency.

Implications

By delivering high‑quality deblurring with a reduced computational footprint, the LMFN could enable real‑time image enhancement on mobile devices, edge cameras and embedded systems where power and memory are limited.

Publication Details

The work is classified under Computer Vision and Pattern Recognition (cs.CV) and Machine Learning (cs.LG) on arXiv. It carries the identifier arXiv:2101.05403 and is accessible via the DOI https://doi.org/10.48550/arXiv.2101.05403. The revised version (v2) reflects updates made in early 2026.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.