Abstract:
This article explores the application of deep learning techniques to improve
the accuracy of feature enhancements in noisy images. A multitasking
convolutional neural network (CNN) learning model architecture has been
proposed that is trained on a large set of annotated images. Various
techniques have been used to process noisy images, including the use of data
augmentation, the application of filters, and the use of image reconstruction
techniques. As a result of the experiments, it was shown that the proposed
model using deep learning methods significantly improves the accuracy of
object recognition in noisy images. Compared to single-tasking models, the
multi-tasking model showed the superiority of this approach in performing
multiple tasks simultaneously and saving training time. This study confirms
the effectiveness of using multitasking models using deep learning for object
recognition in noisy images. The results obtained can be applied in various
fields, including computer vision, robotics, automatic driving, and others,
where accurate object recognition in noisy images is a critical component.
