Application of Improved Non-local Mean Filtering Algorithm in Detection of Oil and Gas Wells

Abstract

As the oil and gas exploration in most parts of China has entered into the middle and late stages, the aging and wear problems of mining equipment have become increasingly prominent. In the process of visual image acquisition of oil and gas well casing damage, due to the influence of the complex environment of light and dust, the image data source noise is serious, and the image recognition model has low accuracy. This paper improves the neighbourhood filtering algorithm block search process with the ant colony pheromone correlation theory, optimizes the block matching process with the Pearson algorithm, and eventually applies the optimized non-local mean filtering algorithm to the oil and gas well casing damage visual detection process. The experimental results show that the improved non-local mean filtering algorithm has a significant impact on the adaptability and effectiveness of the noise reduction effect.