Deep Learning Approach to Image Deblurring and Image Super-Resolution using DeblurGAN and SRGAN
- Kuhlane, Luxolo L, Brown, Dane L, Connan, James, Boby, Alden, Marais, Marc
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
- Authors: Kuhlane, Luxolo L , Brown, Dane L , Connan, James , Boby, Alden , Marais, Marc
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465157 , vital:76578 , xlink:href="https://www.researchgate.net/profile/Luxolo-Kuhlane/publication/363257796_Deep_Learning_Approach_to_Image_Deblurring_and_Image_Super-Resolution_using_DeblurGAN_and_SRGAN/links/6313b5a01ddd44702131b3df/Deep-Learning-Approach-to-Image-Deblurring-and-Image-Super-Resolution-using-DeblurGAN-and-SRGAN.pdf"
- Description: Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur of an image. Image deblurring and super-resolution, as representative image restoration problems, have been studied for a decade. Due to their wide range of applications, numerous techniques have been proposed to tackle these problems, inspiring innovations for better performance. Deep learning has become a robust framework for many image processing tasks, including restoration. In particular, generative adversarial networks (GANs), proposed by [1], have demonstrated remarkable performances in generating plausible images. However, training GANs for image restoration is a non-trivial task. This research investigates optimization schemes for GANs that improve image quality by providing meaningful training objective functions. In this paper we use a DeblurGAN and Super-Resolution Generative Adversarial Network (SRGAN) on the chosen dataset.
- Full Text:
- Date Issued: 2022
Investigating signer-independent sign language recognition on the lsa64 dataset
- Marais, Marc, Brown, Dane L, Connan, James, Boby, Alden, Kuhlane, Luxolo L
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
- Authors: Marais, Marc , Brown, Dane L , Connan, James , Boby, Alden , Kuhlane, Luxolo L
- Date: 2022
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/465179 , vital:76580 , xlink:href="https://www.researchgate.net/profile/Marc-Marais/publication/363174384_Investigating_Signer-Independ-ent_Sign_Language_Recognition_on_the_LSA64_Dataset/links/63108c7d5eed5e4bd138680f/Investigating-Signer-Independent-Sign-Language-Recognition-on-the-LSA64-Dataset.pdf"
- Description: Conversing with hearing disabled people is a significant challenge; however, computer vision advancements have significantly improved this through automated sign language recognition. One of the common issues in sign language recognition is signer-dependence, where variations arise from varying signers, who gesticulate naturally. Utilising the LSA64 dataset, a small scale Argentinian isolated sign language recognition, we investigate signer-independent sign language recognition. An InceptionV3-GRU architecture is employed to extract and classify spatial and temporal information for automated sign language recognition. The signer-dependent approach yielded an accuracy of 97.03%, whereas the signer-independent approach achieved an accuracy of 74.22%. The signer-independent system shows promise towards addressing the real-world and common issue of signer-dependence in sign language recognition.
- Full Text:
- Date Issued: 2022
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