Analyze the Deep Learning Algorithms for Image Enhancement

Abstract

Image reconstruction and image enhancement are basic problems in computer vision, and are of great values for
medical image, satellite remote sensing and image restoration in digital photographs. The reconstruction quality is directly related
to the diagnostic reliability in medical imaging (CT, MRI, X-ray) and to environmental monitoring or in process decision making
in satellite remote sensing, in high stakes applications. Current traditional methods such as interpolation, iterative, and
optimization based methods have the disadvantage of being inherently limited with regards to computational efficiency, failure to
deal with complex profiles of noise and poor performance on low-resolution input data. This paper introduces an original deep
learning architecture that combines a modified 27-layer Convolutional Neural Network (CNN) with the DenseNet121 backbone in
a more synergistic way to overcome these shortcomings. The architecture proposed exploits the reuse of dense features and an
encoder-decoder structure to capture the local texture features and the global structure, hence, facilitating higher fidelity of the
image. The framework is conditioned and tested on the Deep Autoencoder Image Reconstruction benchmark dataset. The results of
the experiment show that the proposed method has an accuracy of 94.6%, a precision of 92.3%, a recall of 91%, and an F1-score
of 90.3%, which is better than the state-of-the-art conventional and deep learning baselines. The work contributions include the
architectural design, the CNN-integration strategy, and a highly rigorous evaluation protocol using multiple metrics. The
innovations have great potential in the further evolution of smart imaging systems in a wide range of real-world applications.