# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 5 Conclusions  
In this paper, we proposed Retinex based image enhancement method via CSC. To realize this, the reflectance function of the Retinex algorithm was designed through CSC. In addition, we applied a SVD to the proposed reflectance function, and a dictionary closer to the basis was constructed. Through this, the Retinex algorithm was designed to preserve more details than the existing methods, and the object function also contributed to the improved quality of the separated image. We also showed that by learning a dictionary from large datasets, we can use a general dictionary without learning a new dictionary from any input image. As shown in the experimental results, the general dictionary is robust and performs adequately for both real and synthetic images. Although we showed that the reflectance dictionary can be effectively constructed using a limited memory and has general applications, it does not contain the optimal number of dictionary filters because the number of dictionary filters has been determined experimentally. Therefore, further research is required to determine the optimal number of dictionary filters for clear and accurate results.  
Figure 12: S-CIELAB Spatial distribution of errors and error histogram: (**a**) Spatial distribution of errors between Figure 10(a) and the sparse coding results in Figure 10(c) that are 30 units or higher, marked in green; (**b**) S-CIELAB histogram distribution between Figure 10(a) and the sparse coding results in Figure 10(c); (**c**) Spatial distribution of errors between Figure 10(a) and the single image CSC results in Figure 10(d) that are 30 units or higher, marked in green; (**d**) S-CIELAB histogram distribution between Figure 10(a) and the single image CSC results in Figure 10(d); (**e**) Spatial distribution of errors between Figure 10(a) and the single image SVD-CSC results in Figure 10(e) that are 30 units or higher, marked in green; (**f**) S-CIELAB histogram distribution between Figure 10(a) and the single image SVD-CSC results in Figure 10(e); (**g**) Spatial distribution of errors between Figure 10(a) and the large data CSC results in Figure 10(f) that are 30 units or higher, marked in green; (**h**) S-CIELAB histogram distribution between Figure 10(a) and the large data SVD-CSC results in Figure 10(f); (**i**) Spatial distribution of errors between Figure 10(a) and the large data SVD-CSC results in Figure 10(g) that are 30 units or higher, marked in green; (**j**) S-CIELAB histogram distribution between Figure 10(a) and the large data SVD-CSC results in Figure 10(g).  
**Author Contributions:** Conceptualization, J.Y. and Y.C.; funding acquisition, Y.C.; investigation, J.Y.; methodology, J.Y.; project administration, Y.C.; software, J.Y.; supervision, Y.C.; writing--original draft, J.Y.; writing--review and editing, J.Y. and Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the Technology Innovation Program (or Industrial Strategic Technology Development Program)(10073229, Development of 4K high-resolution image based LSTM network deep learning process pattern recognition algorithm for real-time parts assembling of industrial robot for manufacturing) funded By the Ministry of Trade, Industry and Energy(MOTIE, Korea).  
**Conflicts of Interest:** The authors declare no conflicts of interest.