## 1 Introduction  
With advancements in imaging, processing, and display technologies in recent years, stereoscopic video entertainment and communication have emerged as promising services of novel visual user experiences such as three-dimensional (3D) television [1], free-viewpoint video [2], and video conferencing [3]. Compared with monocular images, stereoscopic images provide depth perception and engender an immersive user experience [4]. Meanwhile, the immense amount of data generated by stereoscopic imaging requires large storage and transmission capabilities and thus must be efficiently encoded and processed. On the basis of binocular suppression theory [5], higher quality views will be received as the perceived quality of stereo vision by the human visual system (HVS). Thus, mixed resolution (MR) stereoscopic image processing techniques are motivated by binocular perception theory. Specifically, one view of the MR stereoscopic image is provided with full resolution (FR), whereas the other view is degraded by the MR stereoscopic image observation model. To decrease the amount of data while preserving the high definition and stereo vision experience, the low-resolution(LR) view must be super-resolved to a high resolution (HR) at the decoder and display side. In recent years, MR stereoscopic imaging and processing techniques have proven to be effective approaches for stereoscopic imaging and compression [6]. Existing super-resolution (SR) methods are used to reconstruct the FR image from its LR version. These methods are mainly divided into three types; interpolation [7; 8], reconstruction [9; 10], and learning [11; 12; 13; 14; 15]. Among them, the learning-based method has become widely used owing to its outstanding performance. Its basic idea is to establish a mapping relation between the LR and HR image patches and then to find the optimal solution from the LR image. Thus, to study the common prior knowledge between the image patches, most renowned methods adopt the learning-based strategy [16]. [PERSON] et al. [11], for example, adopted the concept of local linear embedding to propose an SR reconstruction method based on neighborhood embedding. Furthermore, the above-mentioned neighborhood embedding is deduced to a more complex sparse coding formulation in [PERSON] et al. 's work [12; 13]. They determined that a linear combination of atoms from an over-complete dictionary can well represent natural image patches. Therefore, after the training process on HR and LR image patches, HR and LR dictionaries are jointly obtained. Then, according to the observed LR patch and LR dictionary, the sparse coefficients are achieved and applied to the HR dictionary to produce the final HR patches. To improve the SR speed while maintaining SR accuracy, [PERSON] et al. [14] used several smaller complete dictionaries to replace the single large over-complete dictionary, thereby greatly reducing the computational cost. In recent years, with the development of deep learning, an increasing number of researchers have employed deep learning for image processing such as image classification [17], object detection [18], and image denoising [19]. Additionally, some researchers have begun establishing a depth model for SR reconstruction. [PERSON] et al. [20] adopted stacked auto-encoders which combined the internal example-based approach to gradually upsample LR images layer by layer. Moreover, [PERSON] et al. [15] combined dictionary learning and neural networks to establish a model of the SR convolutional neural network (SRCNN). This model showed better performance than traditional methods, such as dictionary learning and sparse coding. Furthermore, [PERSON] et al. [21] emphasized the importance of traditional sparse representation. They integrated it into deep learning to further improve the SR results. Although the LR view of the MR stereoscopic image can be directly upsampled by these single-view SR methods, these methods do not take advantage of the correspondence between views for stereoscopic image SR.