Abstract
The accurate diagnosis of medical conditions from low-light images, particularly black-and-white x-rays, is impeded by challenges such as noise, constrained visibility, and a lack of detail. Existing enhancement methods often exacerbate these issues by introducing detail loss, color oversaturation, or higher noise levels. This paper proposes a novel U-Net-based Convolutional Neural Network (CNN) specifically developed to address these challenges in low-light black-and-white medical images. Our designed architecture employs skip connections within the U-Net framework to effectively balance noise reduction with detail information preservation. This makes it possible for the network to learn hierarchical image representations while retaining important features for diagnosis. The trained network accomplishes real-time image enhancement, enabling immediate visual improvement during diagnosis and perhaps assisting radiologists in making faster and more accurate findings. Our approach illustrates a significant improvement in image quality and outperforms traditional methods in terms of noise reduction and detail preservation. This study holds significant potential to improve medical image analysis and diagnosis, potentially leading to enhanced patient care and earlier interventions.
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Hussain, S.A., Chalicham, N., Garine, L. et al. Low-Light Image Restoration Using a Convolutional Neural Network. J. Electron. Mater. (2024). https://doi.org/10.1007/s11664-024-11079-9
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DOI: https://doi.org/10.1007/s11664-024-11079-9