Dynamic Negation of Basic Belief Assignments in Dempster-Shafer Theory with Applications in Classification and Low-Light Image Enhancement

The effective modelling of uncertain information is the key to improving the level of decision-making. Dempster-Shafer theory (DST) can express unknown and partial reliability explicitly. However, existing methods based on DST focus mostly on the accumulation of positive evidence and ignore the counterbalancing effect of negative evidence on the belief distribution, which leads to one-sidedness of decision-making in scenarios of intensified conflict. To this end, this paper proposes a dynamic negation of basic belief assignment (DNBBA), which measures the strength of refutation. In addition, we analyse the properties of DNBBA. On this basis, a decision-making network based on DNBBA (DNBBA-DNet) is proposed, which achieves the migration of the decision paradigm from positive accumulation to dialectical checks and balances. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods on benchmark datasets, enhancing the classification accuracy and overall performance of low-light image enhancement (LLIE) tasks.