Patch orientation‐specified network for learning‐based image super‐resolution

Abstract

Learning-based image super-resolution is considered as a promising solution to reconstruct a high-resolution image from a low-resolution image. To improve the super-resolution performance dramatically, this Letter focuses on the effect of training dataset on the performance and proposes an image super-resolution scheme based on patch orientation-specified network. In particular, a deep neural network is trained using patches with a specific orientation and angular transformation is combined with the neural network to cope with various orientations in input patches. Experimental results show the suggested network model is superior to existing state-of-the-art super-resolution alternatives.

Trends in Super-High-Definition Imaging Techniques Based on Deep Neural Networks

“…Contrary to this trend, it is noted that the method can improve the clarity of the input low-resolution patch for a specific direction while training a neural network model based on a patch dataset with a specific direction. Moreover, by retraining the model parameters of the existing super-resolution technique based on the convolutional neural network (CNN) for each direction (0 to 180 • ), it is possible to achieve super-resolution performance comparable to that expected in [10]. However, storing a large number of models in all patch directions not only requires a huge amount of memory but also involves considerable computational complexity in the training process.…”

“…However, storing a large number of models in all patch directions not only requires a huge amount of memory but also involves considerable computational complexity in the training process. To alleviate this problem, a patch-orientation-specified network (POSNet) is developed in [10] by constructing a dataset with a specific direction and an angle transformation in the same direction as the constructed dataset is applied to the input patch. Additionally, a new patch orientation-specified neural network system is proposed by combining this angle conversion technique with a DNN specially designed for super-resolution.…”

“…Consequently, the trained CNN architecture with multiple convolutional layers and non-linear activation functions plays a key role in solving the ill-posed super-resolution problem in (4) by exploiting both low-level features and high-level features. Moreover, to facilitate both unidirectional and multidirectional input patches in the technique for super-resolution imaging, parallel designing of the patch orientation-specified and unspecified DNNs was proposed, as shown in Figure 13 [10]. The super-resolution technique comprises two parallel networks: (i) a specified neural network for super-resolution of a unidirectional input patch with high directivity (HD) and (ii) a non-specified neural network for the super-resolution of a multi-directional input patch with low directivity (LD).…”

“…The super-resolution technique comprises two parallel networks: (i) a specified neural network for super-resolution of a unidirectional input patch with high directivity (HD) and (ii) a non-specified neural network for the super-resolution of a multi-directional input patch with low directivity (LD). Unlike the existing super-resolution neural networks, the proposed parallel neural networks [10] are adaptively applied according to the input patch orientation. Moreover, to facilitate both unidirectional and multidirectional input patches in the technique for super-resolution imaging, parallel designing of the patch orientation-specified and unspecified DNNs was proposed, as shown in Figure 13 [10].…”

“…DNNs was proposed, as shown in Figure 13 [10]. The super-resolution technique comprises two parallel networks: (i) a specified neural network for super-resolution of a unidirectional input patch with high directivity (HD) and (ii) a non-specified neural network for the super-resolution of a multi-directional input patch with low directivity (LD).…”