Mr. Esau A. Hervert Hernandez Profile

Esau A. Hervert Hernandez

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Proceedings Article | 27 May 2022 Presentation + Paper

Deep learning architecture search for real-time image denoising

Esau Hervert Hernandez, Yan Cao, Nasser Kehtarnavaz

Proceedings Volume 12102, 1210205 (2022) https://doi.org/10.1117/12.2620349

KEYWORDS: Image processing, Image denoising, Denoising, Network architectures, Video, Neural networks, Data modeling, Video processing, Computer programming, Video acceleration

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Deep learning has been widely used in recent years to accomplish many tasks such as image classification, natural language processing, and image denoising amongst others. However, the process to create deep neural networks by trial and error can often be very repetitive and time consuming and it is not clear if the entire network architecture space is explored towards finding an optimum architecture. This paper presents a systematic and automatic way to design or find an optimal architecture of deep neural networks. First, a sensitivity analysis is carried out on the parameters of interest of a network in order to identify those parameters which are most influential to the performance of the network. A search space is defined based on these parameters. Reinforcement learning is then used to find an optimal architecture within this search space. In this paper, our developed method of finding an optimal network architecture is applied to the problem of image denoising. In particular, the emphasis is placed on the Densely Connected Hierarchical Network (DHDN). A resulting network, named ENAS-DHDN, is shown to marginally outperform the original network suggesting that the original network is close to optimal. After finding an optimal network, it is used to estimate the time to process Standard Definition (SD) and High Definition (HD) videos with a frame rate of 30fps indicating that real-time video denoising at the SD resolution is achievable.

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