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Title: | Single Image De-raining Using Convolutional Neural Network |
Authors: | Das, Rajib Lochan Mandal, Srimanta Gajera, Pinak |
Keywords: | Rain streaks image de-raining contextual information residual map synthetic and real-world rainy image |
Issue Date: | 2023 |
Publisher: | Dhirubhai Ambani Institute of Information and Communication Technology |
Citation: | Gajera, Pinak (2023). Single Image De-raining Using Convolutional Neural Network. Dhirubhai Ambani Institute of Information and Communication Technology. viii, 34 p. (Acc. # T01137). |
Abstract: | Rain streaks vary in size, quantity, and direction, making removing them from individualimages difficult. Recent advancements in deep learning, especially thoseusing CNN-based techniques, have shown promising results in addressing this issue.However, the requirement for additional consideration of the rain streaks locationinformation in the image is a significant drawback of these methods. Methodsbased on deep learning have proven to be quite effective in handling syntheticand real-world rainy images. These methods use convolutional neural networks(CNNs) to their full potential to learn the correspondence between rainy and rainfreeimages. We typically use an encoder-decoder architecture where the encoderpulls features from the rainy image and then creates the rain-free image using thelearned features. These algorithms can efficiently learn the complicated correlationsbetween rain streaks and ground truths by training on large-scale datasetsthat combine images with and without rain. End-to-end methods aim to train asingle model that converts the rainy image into its rain-free counterpart withoutexplicitly decomposing it into the rain and the background components. Additionally,researching end-to-end approaches offers a fascinating way of improvingthe de-raining algorithm�s efficiency. More effective and efficient techniques forremoving rain streaks from single images will probably be developed when thisresearch study continues to be investigated. |
URI: | http://drsr.daiict.ac.in//handle/123456789/1196 |
Appears in Collections: | M Tech Dissertations |
Files in This Item:
File | Size | Format | |
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202111063.pdf | 3.25 MB | Adobe PDF | View/Open |
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