Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS

• URL: http://arxiv.org/abs/2009.01371v2
• Date: Fri, 22 Jan 2021 18:48:30 GMT
• Title: Real Image Super Resolution Via Heterogeneous Model Ensemble using GP-NAS
• Authors: Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding
• Abstract summary: We propose a new method for image superresolution using deep residual network with dense skip connections. The proposed method won the first place in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge.
• Score: 63.48801313087118
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With advancement in deep neural network (DNN), recent state-of-the-art (SOTA) image superresolution (SR) methods have achieved impressive performance using deep residual network with dense skip connections. While these models perform well on benchmark dataset where low-resolution (LR) images are constructed from high-resolution (HR) references with known blur kernel, real image SR is more challenging when both images in the LR-HR pair are collected from real cameras. Based on existing dense residual networks, a Gaussian process based neural architecture search (GP-NAS) scheme is utilized to find candidate network architectures using a large search space by varying the number of dense residual blocks, the block size and the number of features. A suite of heterogeneous models with diverse network structure and hyperparameter are selected for model-ensemble to achieve outstanding performance in real image SR. The proposed method won the first place in all three tracks of the AIM 2020 Real Image Super-Resolution Challenge.

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