Research on the Implementation and Optimization of Image Filtering Algorithm Based on OpenGL ES

Image filtering algorithms have wide applications in such fields as machine learning, image processing, and image recognition.They play an important role in reducing "salt and pepper" noise, image binarization, edge recognition, and feature extraction. Although common image filtering algorithms are implemented in the OpenCV open source library, a significant gap in performance exists compared with other platforms on the Android platform. With the rapid development of embedded platforms, the performance requirements for filtering algorithms on embedded platforms have become increasingly high in practical applications. Therefore, starting with filtering algorithms with wide application scenarios, such as morphological filtering, box filtering, threshold filtering, compression filtering, and arithmetic filtering, a series of high-performance image filtering algorithms designed for the Android platform based on OpenGL ES are developed and implemented. OpenGL ES calculation shaders are used to accelerate the algorithm in parallel, using texture objects for memory optimization, and in-depth optimization in image boundary processing, image data types, and data communication is conducted. This approach resulted in better performance. The optimized image filtering algorithm is compared with the corresponding algorithm in the open-source OpenCV library. The experimental results show that the overall performance of the image filtering algorithm based on the Android platform using the OpenGL ES interface is significantly better than the performances of the relevant algorithms in the OpenCV library. The larger the image size, the more obvious the computational advantage. The maximum performance improvement is 110.018 times that of the corresponding algorithm in the OpenCV library.