Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/1196
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

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