<p>This paper introduces a novel strongly convergent subgradient extragradient algorithm for solving variational inequality problems in real Hilbert spaces. The proposed algorithm is designed with a triple mechanism for efficiency: a Nesterov-type inertial term for acceleration, a correction term for stability, and an adaptive step size for practicality. Its principal computational advantage lies in requiring only one evaluation of the underlying operator per iteration, a significant improvement over existing weakly and strongly convergent subgradient extragradient methods, which typically require two evaluations and are therefore more costly for large-scale applications. Under standard assumptions of pseudo-monotonicity and Lipschitz continuity, we prove the strong convergence of the generated sequence to a solution of the variational inequality problem. Furthermore, we establish a linear convergence rate for a variant of our algorithm, a result that, to our knowledge, is unprecedented for this class of methods with single-operator evaluation. The practical superiority of the algorithm is demonstrated through comprehensive numerical experiments on finite and infinite dimensional academic problems, as well as on large-scale applied problems in image restoration, where it consistently outperforms state-of-the-art alternatives in both speed and image quality.</p>