This project represents a major step forward in high-resolution image analysis, with the primary goal of classifying image quality by accurately identifying noise levels. Using a sophisticated combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) architectures, the initiative focuses on distinguishing and categorising different noise patterns within images. This accuracy is critical given the complexity of high-resolution imaging.
The success of the project is demonstrated by its ability to outperform established benchmarks such as ResNet18, ResNet32, VGG-19 and CoAtNet on several key metrics, including accuracy, precision, F1 score and recall. This not only demonstrates the superior performance of the neural network in noise level classification, but also sets new standards for image quality assessment.
The impact of this project is significant, particularly in areas where image fidelity is paramount. It provides a reliable tool for assessing image quality, which is critical in many scientific and industrial applications. By pioneering the use of CNN and LSTM in deep learning for this purpose, the project paves the way for further innovation in specialised data analysis, and highlights the potential of advanced neural networks to improve the accuracy of image quality classification.

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• Asia and Pacific

• Youth
• Researchers and Scientists

The project can only be replicated limited to network architecture . The person needs to know the network architecture when he read from the publication. However, he still needs to obtain the relevant image dataset to train the dataset to replicate the project

Yes, this project is sustainable. This project can be trained with more image data to improve it's performance.

A network dedicated to classifying and improving the quality of scientific images can promote the values of the WSIS within a community by providing better access to accurate scientific data, which supports education and research standards. This improvement of imaging in various research fields, from material sciences to biological studies, supports the creation of a community that values knowledge and inclusiveness. The technology is essential for quality assurance in industrial sectors, underpinning economic growth through high-precision manufacturing and fostering innovation. In addition, through the dissemination of this advanced imaging technology and its findings, the network can narrow the digital divide and provide capacity-building opportunities for local researchers and students. It embodies the principles of the WSIS by making scientific information and tools more accessible and by promoting the responsible use of technology for the benefit of society.