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- W4377970802 abstract "Unwanted reflections commonly degrade the clarity of images taken via glass windows. The elimination of that particular reflection is essential for enhancing image quality. Due to the poorly worded challenge, many solutions require many input photographs, which in practice looks to be difficult. Users using the alternate solution must take numerous images from various perspectives. To decrease this problem, we thought about employing the cascaded network that is a series network model for the transmission and reflection layers. A straightforward approach for a cascade model on IRR is to educate one network to produce a forecasted transmission that act because the auxiliary data of the following network by continuing the method iteratively utilizing subnetworks that enhance the forecast. After passing through all the layers of network, there will be some losses present in an image which produces blurry images. In order to remove losses, the image is moved to the initial step." @default.
- W4377970802 created "2023-05-25" @default.
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- W4377970802 date "2023-01-23" @default.
- W4377970802 modified "2023-09-30" @default.
- W4377970802 title "Image Reflection Removal using Iterative Boost Convolutional LSTM Network through Feature Loss" @default.
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- W4377970802 doi "https://doi.org/10.1109/iccci56745.2023.10128272" @default.
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