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- W4293203413 abstract "The paper proposed a comparative study for various image restoration techniques using deep learning models. While we capture an image, there is very high chances that the image might get disturb due to some reasons like shaking of hand makes the image blurry and light intensity makes the image very bright or very dark, the device used to capture the image is not up to the mark to take clear and perfect picture, etc. So, due to these reasons, the quality of image goes degraded, unsatisfactory, and not able to take that moment with accuracy. This problem of capturing or accepting noisy images leads to an increasing requirement for a quick solution. So, to overcome with all these problems and get the perfect and clear image, image restoration is the solution. The paper provides detailed insight regarding restoration techniques, majorly focusing on convolution neural network (CNN). At the end, the paper provides an analysis based on some deep learning techniques to get an optimal choice for restoration of image." @default.
- W4293203413 created "2022-08-27" @default.
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- W4293203413 date "2022-01-01" @default.
- W4293203413 modified "2023-10-18" @default.
- W4293203413 title "CNN-Based Optimal Image Restoration and Comparative Approaches" @default.
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- W4293203413 doi "https://doi.org/10.1007/978-981-19-1324-2_19" @default.
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