VR Scene Detail Enhancement Method Based on Depth Reinforcement Learning Algorithm

Abstract

Abstract In virtual reality, due to factors such as light sources and surface materials of objects, the details of the scene exhibit extremely complex changes, making it difficult to capture environmental modeling relationships and reducing the quality of scene details. Therefore, a VR scene detail enhancement method based on deep reinforcement learning algorithm is proposed. Using the Total Variation (TV) denoising algorithm to decompose the VR scene, the VR scene is divided into two parts: noisy and non-noisy, to complete the noise removal. Based on the denoised VR scene, a Hessian matrix is constructed to extract VR scene features using the SURF algorithm. Using deep reinforcement learning algorithms to train and process the extracted VR visual features, introducing meta-learning within the gradient descent method, updating the parameters of the deep reinforcement learning Expose framework, and accelerating the training speed of the deep reinforcement learning Expose framework. By designing L1 loss, structural similarity loss, content perception loss, and sharpness loss functions, the enhancement effect of VR visual details can be improved. The experimental results show that the proposed method enhances the gray-scale values and distribution of VR scene detail images to be higher and more uniform. When the magnification is different, the studied method can effectively enhance the signal-to-noise ratio of VR scenes. Therefore, it indicates that the VR scene detail enhancement effect of the method proposed in this article is good.

Keywords

• Deep reinforcement learning
• VR scene
• Detail enhancement
• Expose framework
• Gradient descent method
• Meta learning