Research on Image Signal Enhancement and Denoising Algorithms for Intelligent Communication Systems

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid $\mathrm{C}++$ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems.