Abstract
In today's digital age, people often use images to convey information. However, during the process of sending images, they can become damaged for various reasons, causing the images to lose their original meaning. In such cases, image repair is necessary to restore the damaged areas to their original, undamaged appearance. Traditional image repair methods often struggle to produce effective results for highly damaged images. However, image repair using deep learning techniques can address these challenges effectively. This paper will detail the impact of image repair on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), Convolutional Recurrent Neural Networks (CRNN), and other models, as well as the advantages and disadvantages of each approach. From the data, it is evident that combining multiple models for image repair is the most effective method. This approach reduces the consumption of hardware resources and time required for image repair, thereby improving the overall efficiency of the process.
Published Version
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