The week's pick
Reseach Article
A Deep Learning Approach using Convolutional Autoencoders for Image Deblurring
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 46 |
| Year of Publication: 2024 |
| Authors: Geeta B., R.L. Raibagkar |
10.5120/ijca2024924126
|
Geeta B., R.L. Raibagkar . A Deep Learning Approach using Convolutional Autoencoders for Image Deblurring. International Journal of Computer Applications. 186, 46 ( Nov 2024), 56-59. DOI=10.5120/ijca2024924126
Abstract
Image de-blurring is a crucial task in computer vision with numerous applications in various fields such as medical imaging, surveillance, and autonomous vehicles. This paper presents a novel approach to image deblurring using convolutional autoencoders (CAEs). The proposed method leverages the power of deep learning and unsupervised learning to automatically learn features and reconstruct sharp images from blurry inputs. By training the CAE on pairs of blurry and corresponding sharp images, the network learns to capture the underlying structure and features essential for deblurring. To evaluate the effectiveness of proposed approach, extensive experiments were conducted on standard datasets consisting of sharp and blurred images.
References
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