Fidelity-Guided Restoration: Balancing Noise Reduction and Texture Preservation for Enhanced Image Details

Image restoration algorithms strive to reduce noise while retaining crucial Features and textures. Integrating the reduction of noise with texture retention remains an ongoing difficulty image restoration. In this study, we introduce Fidelity-Guided Restoration (FGR), a novel method that incorporates fidelity regularization to improve restoration quality. FGR utilizes the fidelity between the degraded and restored images to guide the restoration process. This technique encourages the algorithm to maintain fine details and textures present in the degraded input while effectively reducing noise. Implemented using convolutional neural networks (CNNs), FGR captures both low-level and high-level features essential for learning intricate patterns and textures. Additionally, perceptual loss functions are employed during training to further enhance the preservation of higher-level features. Our research findings reveal that FGR exceeds the present modern methods in terms of detail preservation and texture enhancement. Comparative evaluations on benchmark datasets confirm that FGR achieves a superior balance between noise reduction and texture preservation. Ablation studies underscore the critical role of fidelity regularization and perceptual loss functions in the restoration process. In summary, Fidelity-Guided Restoration offers a promising solution for image restoration tasks that require both noise reduction and texture preservation. By focusing on fidelity and perceptual features, FGR produces visually appealing and high-quality restored images, making it suitable for a variety of image restoration challenges.