Frequency-domain deconvolution in deep learning
| dc.contributor.advisor | Nicolls, Frederick | |
| dc.contributor.author | Meyer, Moegamad | |
| dc.date.accessioned | 2025-09-10T10:46:42Z | |
| dc.date.available | 2025-09-10T10:46:42Z | |
| dc.date.issued | 2025 | |
| dc.date.updated | 2025-09-10T10:22:26Z | |
| dc.description.abstract | This dissertation presents an exhaustive exploration of a novel approach to deep learning in computer vision tasks: the frequency-domain deconvolution operation. Recognizing the unparalleled success of convolutional neural networks (CNNs) in the realm of computer vision, we critically evaluate the performance and computational demands of traditional convolution operations against the proposed deconvolution method. Using a systematic approach, we apply the deconvolution layer to two quintessential computer vision problems: image classification and single image super resolution (SISR). The results demonstrate the deconvolution layer's potential in certain scenarios, with marked improvements observed in image classification. For SISR tasks, though advantages were noticed under specific configurations, the traditional CNNs still demonstrated their robustness. Additionally, the dissertation touches upon the layer's computational demands, revealing an increased computational overhead for the deconvolution layer. Encouragingly, the layer demonstrated promising attributes like learning long-range filters and isolating objects from backgrounds effectively. Concluding with avenues for future research, this dissertation acts as a stepping stone in the uncharted territory of deconvolution operations, emphasising innovation alongside evaluation in the dynamic world of deep learning. | |
| dc.identifier.apacitation | Meyer, M. (2025). <i>Frequency-domain deconvolution in deep learning</i>. (). University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/41745 | en_ZA |
| dc.identifier.chicagocitation | Meyer, Moegamad. <i>"Frequency-domain deconvolution in deep learning."</i> ., University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2025. http://hdl.handle.net/11427/41745 | en_ZA |
| dc.identifier.citation | Meyer, M. 2025. Frequency-domain deconvolution in deep learning. . University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering. http://hdl.handle.net/11427/41745 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Meyer, Moegamad AB - This dissertation presents an exhaustive exploration of a novel approach to deep learning in computer vision tasks: the frequency-domain deconvolution operation. Recognizing the unparalleled success of convolutional neural networks (CNNs) in the realm of computer vision, we critically evaluate the performance and computational demands of traditional convolution operations against the proposed deconvolution method. Using a systematic approach, we apply the deconvolution layer to two quintessential computer vision problems: image classification and single image super resolution (SISR). The results demonstrate the deconvolution layer's potential in certain scenarios, with marked improvements observed in image classification. For SISR tasks, though advantages were noticed under specific configurations, the traditional CNNs still demonstrated their robustness. Additionally, the dissertation touches upon the layer's computational demands, revealing an increased computational overhead for the deconvolution layer. Encouragingly, the layer demonstrated promising attributes like learning long-range filters and isolating objects from backgrounds effectively. Concluding with avenues for future research, this dissertation acts as a stepping stone in the uncharted territory of deconvolution operations, emphasising innovation alongside evaluation in the dynamic world of deep learning. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - Convolutional neural networks LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - Frequency-domain deconvolution in deep learning TI - Frequency-domain deconvolution in deep learning UR - http://hdl.handle.net/11427/41745 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/41745 | |
| dc.identifier.vancouvercitation | Meyer M. Frequency-domain deconvolution in deep learning. []. University of Cape Town ,Faculty of Engineering and the Built Environment ,Department of Electrical Engineering, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41745 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Electrical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | Convolutional neural networks | |
| dc.title | Frequency-domain deconvolution in deep learning | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |