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- Title
Multimodal image enhancement using convolutional sparse coding.
- Authors
Ahmed, Awais; Kun, She; Ahmed, Junaid; Hayat, Shaukat; Khan, Abdullah Aman
- Abstract
This paper proposes a wavelet domain-based method for multispectral image super-resolution. The stationary wavelet transform is proposed to decompose the multispectral image into directional wavelet components and for each wavelet component, a joint dictionary learning algorithm is proposed. Using sparse and redundant representations, the proposed approach helps capture intrinsic multispectral features using wavelet domain learning utilizing the up-sampling property of (SWT). The proposed method can learn and recover those image features more accurately. In order to validate the proposed method, we conducted comprehensive experiments. Moreover, we present a comparison of our proposed method with state-of-the-art algorithms over PSNR and SSIM evaluation parameters. The results of the experiments indicate that the proposed method outperforms state-of-the-art methods.
- Subjects
IMAGE intensifiers; WAVELET transforms; MACHINE learning; HIGH resolution imaging; MULTISPECTRAL imaging
- Publication
Multimedia Systems, 2023, Vol 29, Issue 4, p2099
- ISSN
0942-4962
- Publication type
Article
- DOI
10.1007/s00530-023-01074-1