Research on Image Denoising Based on Combined Adaptive GP and DCNN

In the process of image acquisition and transmission, it is easy to be interfered by electronic equipment and light, resulting in noise, which affects people's acquisition and recognition of information. In image processing and computer vision, image denoising is an important step. Based on this, an image denoising algorithm combining adaptive GP (Gaussian filter, GF) and DCNN (Denoising Convolutional Neural Network, DCNN) is proposed. Through the adaptive GF preprocessing step, the noisy image is preliminarily smoothed and enhanced in detail, which provides a more favorable data basis for the subsequent DCNN deep denoising operation. Experimental results show that, compared with the traditional denoising algorithm, the proposed algorithm performs well in noise suppression, and can significantly improve the peak signal-to-noise ratio (PSNR) of the image while maintaining the image details. This achievement not only shows the excellent performance of the algorithm in the field of image denoising, but also further verifies its theoretical value in the field of image processing, and provides a strong support and reference for the development of related technologies in the future.