Learning Based Single Image Super Resolution Using Discrete Wavelet Transform

2017 ◽  
pp. 462-472
Author(s):  
Selen Ayas ◽  
Murat Ekinci
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Wavelet-Integrated Deep Networks for Single Image Super-Resolution

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 553 ◽  
Author(s):  
Faisal Sahito ◽  
Pan Zhiwen ◽  
Junaid Ahmed ◽  
Raheel Ahmed Memon
Keyword(s):  
Wavelet Transform ◽  
Similarity Index ◽  
Super Resolution ◽  
Training Data ◽  
Discrete Wavelet ◽  
Single Image ◽  
Data Set ◽  
Proposed Model ◽  
Image Super Resolution ◽  
Single Image Super Resolution

We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

scholarly journals Wavelet Domain Multidictionary Learning for Single Image Super-Resolution

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaomin Wu ◽  
Jiulun Fan ◽  
Jian Xu ◽  
Yanzi Wang
Keyword(s):  
Wavelet Transformation ◽  
Super Resolution ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Relationship Of ◽  
The Given ◽  
The Relationship

Image super-resolution (SR) aims at recovering the high-frequency (HF) details of a high-resolution (HR) image according to the given low-resolution (LR) image and some priors about natural images. Learning the relationship of the LR image and its corresponding HF details to guide the reconstruction of the HR image is needed. In order to alleviate the uncertainty in HF detail prediction, the HR and LR images are usually decomposed into 4 subbands after 1-level discrete wavelet transformation (DWT), including an approximation subband and three detail subbands. From our observation, we found the approximation subbands of the HR image and the corresponding bicubic interpolated image are very similar but the respective detail subbands are different. Therefore, an algorithm to learn 4 coupled principal component analysis (PCA) dictionaries to describe the relationship between the approximation subband and the detail subbands is proposed in this paper. Comparisons with various state-of-the-art methods by experiments showed that our proposed algorithm is superior to some related works.

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