Schrödinger Bridges (SB) are a novel class of diffusion-based methods adapted to image restoration tasks such as super-resolution, denoising, and image colorization. Unlike traditional denoising diffusion models, Schrödinger Bridges are designed so that their stationary distribution is centered on the input image to be enhanced rather than pure noise. However, their optimization process still relies on a mean squared error (MSE) criterion between the prediction and the applied noise. In this work, we propose an improvement to the Schrödinger Bridge models by enriching their loss function with additional information. Specifically, we introduce a dynamically weighted style-based loss that leverages a pre-trained model to extract high-level feature maps for comparison against ground-truth embeddings. The enhancement improves the performance of Schrödinger Bridge architectures on a range of benchmarks, demonstrating the effectiveness of our approach in advancing image enhancement capabilities.