In this paper, we propose a novel deterministic model based on the Brownian Bridge framework, leveraging Stochastic Differential Equations (SDEs). Our approach is designed to address the limitations of stochasticity present in existing Bridge models. Specifically, we introduce a method where two neural networks are employed: one for predicting the score function and the other for estimating the noise. By doing so, our model ensures a deterministic outcome, which is crucial for tasks requiring consistency and precision, such as super-resolution and medical image reconstruction. Our key contributions are as follows