MolexNet: Real-Time Recurrent-Based Object Removal and Enhancement Network

Abstract

Abstract In the contemporary realm of human portrait image segmentation tasks, achieving precise separation of foreground and background within intricate background and foreground edge scenarios poses a formidable challenge. In this study, we introduce a robust, efficient, and remarkably accurate image segmentation model named "MolexNet". In the domain of human portrait image segmentation, our model outperforms the majority of network architecture models in the field. What sets our model apart from conventional lightweight segmentation models is the clever fusion of features at different scales from the input image within the decoder. This enhancement significantly enhances processing speed while maintaining the quality of image segmentation, resulting in exceptional results for human portrait image segmentation tasks. We rigorously evaluate this model using various test datasets, and it consistently exhibits outstanding performance in both image and video segmentation tasks. It adeptly handles complex scenes and dynamic foreground elements, yielding satisfying segmentation results. Moreover, due to its ingenious design concepts, precise optimization strategies, and efficient model structure, our model maintains a high level of accuracy. We also conduct comparative analyses with other open-source architectures to underscore the superior performance and computational efficiency of our model. The model presented in this paper serves as a valuable reference and source of inspiration for research and practical applications in the field of image segmentation.