Deep Attention-based Lightweight Network For Aerial Image Deblurring

Wang, Suhe; Liu, Bo

doi:10.1109/icpr56361.2022.9956671

Cited by 1 publication

(1 citation statement)

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“…Although the generalization performance of the network has been verified on multiple datasets, it has low practical application value due to its large number of parameters and computational delays. Moreover, nearly all of the representative models for aerial image enhancement are based on single-task design ( Wang and Liu, 2022 ). Therefore, research on an all-in-one framework is still very necessary in this field.…”

Section: Introductionmentioning

confidence: 99%

All-in-one aerial image enhancement network for forest scenes

et al. 2023

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Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

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Section: Introductionmentioning

confidence: 99%