Image Super-Resolution with Deep Dictionary

• URL: http://arxiv.org/abs/2207.09228v1
• Date: Tue, 19 Jul 2022 12:31:17 GMT
• Title: Image Super-Resolution with Deep Dictionary
• Authors: Shunta Maeda
• Abstract summary: We propose an end-to-end super-resolution network with a deep dictionary (SRDD) We show that explicit learning of high-resolution dictionary makes the network more robust for out-of-domain test images.
• Score: 12.18340575383456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the first success of Dong et al., the deep-learning-based approach has become dominant in the field of single-image super-resolution. This replaces all the handcrafted image processing steps of traditional sparse-coding-based methods with a deep neural network. In contrast to sparse-coding-based methods, which explicitly create high/low-resolution dictionaries, the dictionaries in deep-learning-based methods are implicitly acquired as a nonlinear combination of multiple convolutions. One disadvantage of deep-learning-based methods is that their performance is degraded for images created differently from the training dataset (out-of-domain images). We propose an end-to-end super-resolution network with a deep dictionary (SRDD), where a high-resolution dictionary is explicitly learned without sacrificing the advantages of deep learning. Extensive experiments show that explicit learning of high-resolution dictionary makes the network more robust for out-of-domain test images while maintaining the performance of the in-domain test images.

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