# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Sparse Coding Methods  
The method of expressing signals as a linear combination of a few basis functions, with most coefficients close to zero, is referred to as sparse coding. In the real-time SR domain, sparse coding methods can be utilized in two steps such as dictionary learning and sparse reconstruction. Dictionary learning entails locating an over-complete set of basis functions, referred to as a dictionary, that can sparsely represent HR picture patches. A training set of HR photos is used to train the dictionary. Various dictionary learning algorithms, such as the K-SVD algorithm and the online dictionary learning method, can be employed. The learned dictionary captures the underlying patterns and structures in HR space, enablingsparse representation to be economical. Secondly, it follows sparse reconstruction. The sparse reconstruction stage uses the learned dictionary to identify the sparse representation of the patches given an LR digital frame. This can be accomplished by solving a sparsity-promoting optimization problem, such as an L1-norm minimization problem. The sparse reconstruction approach improves the representation of high-frequency information that is lacking in the LR input by increasing sparsity.  
Sparse arrays offer better resolution and degrees of freedom compared to traditional uniform linear arrays (ULAs). [PERSON] [72] refutes the claim that sparse arrays need many temporal measurements for accurate parameter estimation. Through a non-asymptotic analysis of the Coarray ESPRIT algorithm, his work shows that coarray manifold scaling reduces estimation errors even with fewer snapshots. For nested arrays with fewer sources than sensors, estimation errors can be minimized with a logarithmic number of snapshots relative to sensors. The study also confirms that sparse arrays can achieve source separation with a minimum distance of \(\Omega(1/P^{2})\) versus \(\Omega(1/P)\) for ULAs, and this highlights their superior noise resilience and sample complexity dependence on the Signal-to-Noise Ratio (SNR) and source power. This approach will guide to efficient deployment in real-time SR.  
In [25], the authors investigated at how incorporating sparsity into image SR might improve the efficiency of SR networks during inference. They proposed the Sparse Mask SR (SMSR) network, which is meant to generate sparse masks and decrease duplicate processing. Specifically, the network employs spatial masks to identify \"important\" parts in the image, whereas channel masks detect redundant channels in less-critical locations. This approach allows the network to choose to avoid duplicates operations, resulting in identical performance but with improved efficiency.  
Discussion and Limitations  
Optimization-based real-time SR approaches are successful, but they have several drawbacks. Gradient descent approaches, despite improving the perception-distortion trade-off, frequently use predetermined degradation models and are hyperparameter-sensitive, restricting their versatility. Bayesian inference, while informative, is computationally demanding and unsuitable for real-time applications. Sparse coding approaches improve high-frequency detail but are strongly reliant on the quality of the learned lexicon. Despite advances such as SMSR to enhance efficiency, obtaining real-time performance without losing picture quality remains difficult, necessitating more adaptable and computationally efficient techniques.