An Adaptive Image-denoising Method Based on Jump Regression and Local Clustering

Image denoising is crucial for reliable image analysis. Researchers from diverse fields have long worked on this, but we still need better solutions. This article focuses on efficiently preserving key image features like edges and structures during denoising. Jump regression analysis is commonly used to estimate true image intensity amid noise. One approach is adaptive smoothing, which uses various local neighborhood shapes and sizes based on empirical data, while another is local pixel clustering to reduce noise while maintaining important details. This manuscript combines both methods to propose an integrated denoising technique.