Nowadays, image processing technology has attracted much attention. This study mainly focuses on image processing algorithms based on deep learning (DL), aiming to solve key technical problems in current fields such as image recognition, image segmentation, and image enhancement. The specific methods include using convolutional neural network (CNN) for image feature extraction, using generative adversarial network (GAN) for image quality enhancement, and exploring the application of autoencoder in image denoising. In addition, the study also involves optimization algorithms for DL models, which improve the efficiency and accuracy of algorithms by improving training strategies and network structures. From the perspective of processing time, although there are some fluctuations, the processing time of most frames is concentrated between 28 and 31 ms, indicating that the model's image recognition and processing speed in real-time video streams is relatively stable and can meet the requirements of real-time performance. This study aims to promote innovation in image processing algorithms, achieve efficient and accurate image processing in complex environments, and play an important role in technological progress and social applications through detailed analysis and experimental verification.