Study on Image Background Removal using Deep Learning

Abstract

Removing image backgrounds is a common job in image processing and computer vision. By isolating the main object from the back, background removal in photographs aims to make it easier to examine or edit the image. There are numerous methods for removing the background from an image, including deep learning, color-based segmentation, and human selection. The U-Net architecture, one of the deep learning-based techniques, has demonstrated encouraging results in image segmentation tasks, including image background removal. A convolutional neural network created for biological image segmentation is known as the U-Net architecture. The design consists of an encoder network that stores the context and a decoder network that generates the segmentation map. The U-shape of the U-Net architecture enables it to record both the overall context and the local specifics of the image. For several picture segmentation tasks, including image background removal, U-Net architecture has undergone modification. The suggested method for removing image backgrounds using U-Net entails training a U-Net model on a dataset of pictures with and without background. Then, using the demonstrated methodology, the backdrop is removed from recent photographs. The suggested method differs from current approaches in various, including its high accuracy and capacity to handle complicated backgrounds. Computer vision, object identification, and photo manipulation are just a few of the uses for the suggested method