# Image Super-Resolution Based on CNN Using Multilabel Gene Expression Programming
## 3 The Proposed Method
### MGEP Classification before CNN Image Super-Resolution  
In the process of image super-resolution reconstruction, the traditional K-means clustering algorithm [20] is used to classify the trained image set to improve the training effect and reduce the training time. In the K-means clustering algorithm, the value of \(K\) needs to be determined in advance,and it cannot be changed during the entire course of the algorithm, which makes it difficult to accurately estimate the value of \(K\) when training high-dimensional data sets.  
We used the MGEP classification algorithm instead of the K-means clustering algorithm, and its predassification model is shown in Figure 3.  
Let \(p\)-many sample pattern pairs (\(x_{k},y_{k}\)) constitute the training set, \(k\) = 1, 2,..., \(p\). According to the definition of the GEP classifier, for sample \(k\), there are \(x_{k}\) = (\(x_{k1}\), \(x_{k2}\),..., \(x_{km}\)) and \(y_{k}\) = (\(y_{k1}\), \(y_{k2}\),..., \(y_{kn}\)), where \(x_{kj}\) is the sample in the attribute \(A_{j}\) (\(j\) = 1, 2,..., \(m\)) and \(y_{ki}\) is the degree of membership of the sample to the category \(C_{i}\) (\(i\) = 1, 2,..., \(n\)). The training set constitutes the set of adaptive instances, and the set of adapted instances of a particular problem forms the adaptive environment of the MGEP algorithm. Under a certain adaptive environment, starting from the initial population, selection, replication, and various genetic operations are performed according to individual fitness to form a new population. This process is repeated until the optimal individual is evolved and decoded to obtain a GEP multilabel classifier. In the MGEP preclassification learning, we find similar samples for LR image blocks in terms of color and texture features, thereby shortening the time of the next precise matching and improving the efficiency and effect of SR image restoration.  
In the training of the CNN, the MGEP algorithm is used to classify the trained image set, classify the approximate images into one category, and reduce the parameter scale of the convolutional neural network model, which can reduce the training time of the network to a certain extent and improve the training efficiency of the CNN. The improved algorithm flow based on SRCNN is shown in Figure 4.  
Figure 3: The Multilabel Gene Expression Programming (MGEP) preclassification model.  
The algorithm uses Wiener filtering to construct a deconvolution layer, which is used to implement multiscale image reconstruction. The deconvolution network part adopts the mirror structure of the convolution network. The purpose is to reconstruct the shape of the input target, so the multilevel deconvolution structure can also capture the shape details of different levels like the convolution network. In the model of the convolutional network, low-level features can describe the rough information of the entire target, such as target position and approximate shape, while more complex high-level features have classification characteristics and also contain more detailed target information.