A 3-D Block Stripe Noise Detection and Removal Method Based on Global Search Optimization and Dense Gabor Filters

Remote sensing images are increasingly being used in military and civilian fields. However, due to the influence of factors such as detector movement and temperature changes during the acquisition of remote sensing images, these images are often contaminated by stripe noise, which has adverse effects on tasks such as inversion, target detection, and semantic segmentation. Therefore, we developed a method for completely removing stripe noise, without destroying necessary information in remote sensing images, based on global search optimization and dense Gabor filters. Unlike other algorithms that directly solve the underlying image, this method applies Gabor filters to estimate stripe noise in the noisy image, locate stripe or nonstripe noise, and then categorize the stripe noise as sparse or dense according to its distribution frequency. Finally, the directionality of the stripe noise, its smoothness along the noise direction, and the local continuity of the underlying image are fully determined. The noisy image is divided into blocks along the stripe noise direction, and the intensity range of the stripe noise is estimated at different positions in each subimage. The stripe noise is directly solved within the estimated intensity range using global search optimization to achieve stripe noise removal. The proposed method can select different solutions for processing according to the type of stripe noise. A large number of experiments were conducted using simulated and real data, and the results demonstrated that the proposed method qualitatively and quantitatively outperformed current state-of-the-art stripe noise removal methods.