Document Type


Publication Title

Information Fusion


The digital transformation in healthcare, propelled by the integration of deep learning models and the Internet of Things (IoT), is creating unprecedented opportunities for improving patient care. However, the utilization of low-resolution images, often generated by IoT devices, introduces biases in the deep learning models, thereby affecting the overall clinical decision-making process. While super-resolution techniques have been extensively employed to transform low-resolution images into high-resolution counterparts, the challenge of achieving highly accurate image restoration remains unresolved. This is especially critical in the medical imaging domain, where even minor inaccuracies can lead to significant biases in model training and, consequently, impact clinical outcomes. Although existing surveys have explored various super-resolution methods and their applications across different fields, a comprehensive review emphasizing the accuracy of image restoration in medical imaging and its subsequent influence on deep learning models is notably lacking. This survey seeks to bridge this gap by offering a systematic review of current state-of-the-art models, highlighting the limitations of existing surveys, and underscoring open questions that merit further research. Specifically, we delve into the intricacies of medical image restoration, identify research gaps and unmet challenges in achieving optimal restoration of medical images, and emphasize the crucial role of developing more precise and resilient super-resolution methods to enhance the quality of medical images and, consequently, the performance of deep learning models in healthcare applications. Ultimately, this survey fosters a deeper comprehension of the prevailing challenges and unresolved questions in the field, thus setting the stage for future research efforts focused on refining medical image restoration and, subsequently, boosting the efficacy of deep learning models in healthcare.



Publication Date



Deep learning, Healthcare, Image reconstruction, Medical image analysis


Open Access version from Elsevier


Uploaded on May 31, 2024