FPGA-based reflection image removal using cognitive neural networks

There is an enormous increase in the resource usage and certain process is required to satisfy the user requirement. Thus, there is a process integration on IoT and data analytics which paves the way for the smart city development. The energy management plays a crucial role based on the processing of image dataset and optimization using the artificial intelligence. To increase the visibility of the image based on the reflection concept, cognitive models are used by relocating the clear glass images. The above process is used widely in computer vision applications as it is ill nature and makes the additional precursors more challenging. Then, eliminating reflections problem is considered for various heuristic observations or other assumptions and it fulfills in practical conditions. In this paper, we generalize the assumptions for issues of reflection based on usage of different information or impose new limitations elimination. The image elimination helps in effective energy utilization by managing the resource through CNN, i.e., optimization techniques of cognitive networks. To overcome the various optimization functions, long computational time required and reflection removal methods, i.e., conventional have not guaranteed their performance. It helps in developing the smart cities as energy plays a significant role as the propose system is integrated with IoT and data analytics. As FPGA system is deployed, it can screen the images by providing effectively the sustainable policies with energy efficient platform through data compression. In the performance analysis, the proposed algorithm is effective as compared with current approaches using CNN methods. Parallel processing of Images in Digital Signal Processing tools will create degradation of the images and it is difficult to achieve high performance, so the implementation is done using FPGA.