1 research outputs found
Lightweight Modules for Efficient Deep Learning based Image Restoration
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 classification.
However, low-level image processing falls under the image-to-image' translation
genre which requires some additional computational modules not present in
classification. This paper seeks to bridge this gap by designing generic
efficient modules which can replace essential components used in contemporary
deep learning based image restoration networks. We also present and analyse our
results highlighting the drawbacks of applying depthwise separable
convolutional kernel (a popular method for efficient classification network)
for sub-pixel convolution based upsampling (a popular upsampling strategy for
low-level vision applications). This shows that concepts from domain of
classification cannot always be seamlessly integrated into image-to-image
translation tasks. We extensively validate our findings on three popular tasks
of image inpainting, denoising and super-resolution. Our results show that
proposed networks consistently output visually similar reconstructions compared
to full capacity baselines with significant reduction of parameters, memory
footprint and execution speeds on contemporary mobile devices.Comment: Accepted at: IEEE Transactions on Circuits and Systems for Video
Technology (Early Access Print) | |Codes Available at:
https://github.com/avisekiit/TCSVT-LightWeight-CNNs | Supplementary Document
at:
https://drive.google.com/file/d/1BQhkh33Sen-d0qOrjq5h8ahw2VCUIVLg/view?usp=sharin