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Multimodal image enhancement using convolutional sparse coding
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AI
DOI：10.1007/s00530-023-01074-1.png)
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Abstract
En 中文
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.
Keywords:
Super-resolution
Wavelet domain
Stationary wavelet transform
Dictionary learning
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