Related papers: Perceptual Image Enhancement for Smartphone Real-Time Applications

Perceptual Image Enhancement for Smartphone Real-Time Applications

URL: http://arxiv.org/abs/2210.13552v2
Date: Thu, 23 Nov 2023 00:46:44 GMT
Title: Perceptual Image Enhancement for Smartphone Real-Time Applications
Authors: Marcos V. Conde, Florin Vasluianu, Javier Vazquez-Corral, Radu Timofte
Abstract summary: We propose LPIENet, a lightweight network for perceptual image enhancement. Our model can deal with noise artifacts, diffraction artifacts, blur, and HDR overexposure. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.
Score: 60.45737626529091
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones.

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