小组新闻

Abstract—Recently, many new results of sharpness assessment for digital images have been achieved, which help to select valuable images from massive images with ragged quality. However, remote sensing images encompass a wide range of scenes with diverse characteristics, and their acquisition is often influenced by blurs and noises. Many commonly used sharpness assessment methods based on uniform metrics face challenges in ensuring both subjective and objective impartiality when applied to remote sensing images. Therefore, a novel method for assessing the sharpness of remote sensing images based on a deep multi-branch network considering scene features is proposed. In the method, a multi-task module, comprising scene classification and sharpness assessment tasks, is proposed to comprehensively consider the potential impact of diverse scene characteristics on the assessment of sharpness. The accuracy of sharpness assessment is improved by sharing features that reflect the correlation between the scene and sharpness. To overcome the issue of imbalanced task predictions during the joint training of multiple tasks, a total loss function using gradient balance strategy is designed. In addition, the improved attention module and the feature fusion module are used to better utilize feature information at different scales. Experimental results obtained from datasets demonstrated that the proposed method can outperform the existing comparable methods and achieve satisfactory results, proving its feasibility and effectiveness.