In this paper, we propose a novel loss-based optimization strategy for image deblurring, leveraging the Attention U-Net architecture and a hybrid training objective that enhances both perceptual and structural reconstruction quality. Our approach is specifically tailored to the HIDE dataset and achieves improved performance in two key metrics: Structural Similarity Index and Peak Signal-to-Noise Ratio. These metrics are essential for evaluating the quality of image restoration, particularly in machine learning and computer vision applications. Experimental results on the HIDE dataset show that our method achieves competitive results compared to previously published models, providing a meaningful advancement in the field of image deblurring.