2021
DOI: 10.1109/tcsvt.2020.3007723
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Lightweight Modules for Efficient Deep Learning Based Image Restoration

Abstract: Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks which present a massive computational overhead on resource constrained platform like a mobile phone. In this paper, we propose several lightweight low-level modules which can be used to create a computationally low cost variant of a given baseline model. Recent works for efficient neural networks design have mainly focused on classificat… Show more

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Cited by 32 publications
(4 citation statements)
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“…When the size of the model reaches a certain level, it is difficult to obtain significant performance improvement by simply increasing the size of the model. Grouped convolution and depthwise convolution have been applied to many lightweight models (Zhou et al 2020, Lahiri et al 2021, and we also demonstrated their excellent ability in ECG denoising. ECG characteristic waveforms contain rich medical and physiological information, and it is important to retain effective characteristic waveforms while removing noise.…”
Section: Discussionmentioning
confidence: 75%
“…When the size of the model reaches a certain level, it is difficult to obtain significant performance improvement by simply increasing the size of the model. Grouped convolution and depthwise convolution have been applied to many lightweight models (Zhou et al 2020, Lahiri et al 2021, and we also demonstrated their excellent ability in ECG denoising. ECG characteristic waveforms contain rich medical and physiological information, and it is important to retain effective characteristic waveforms while removing noise.…”
Section: Discussionmentioning
confidence: 75%
“…However, most of the methods are limited in real-world applications by their huge computational cost. To handle this issue, some lightweight and efficient SISR methods have been presented [17], [25]- [31]. For example, IDN [32] used an information distillation network to selectively fuse features, and then IMDN [33] improved it to build a lighter model.…”
Section: A Lightweight Sisr Methodsmentioning
confidence: 99%
“…It is noted that the differences in terms of image quality and video specification between the clean virtual contents and real displayed images/videos can appear in VR devices [66], [67]. These gaps can be filled effectively with AI-empowered image restoration methods, such as blur estimation, hazy removal, color correction, and texture reconstruction, but the computational complexity should satisfy the real-time video processing speed (usually measured by frames per second -FPS metric) [68] to guarantee high-class user experience in the metaverse. Image enhancement has been widely considered for XR with some common tasks, such as contrast increment and superresolution construction.…”
Section: B Machine Visionmentioning
confidence: 99%