Показати скорочений опис матеріалу
dc.contributor.author | Kuspys, Nazarii![]() |
|
dc.date.accessioned | 2024-02-14T11:46:22Z | |
dc.date.available | 2024-02-14T11:46:22Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Kuspys, Nazarii. Research of Data Augmentation Approaches for Enhancing Classification Model Performance / Nazarii Kuspys; Supervisor: Volodymyr Karpiv; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 41 p.: ill. | uk |
dc.identifier.uri | https://er.ucu.edu.ua/handle/1/4431 | |
dc.language.iso | en | uk |
dc.title | Single Image Motion Deblurring for Mobile Devices | uk |
dc.type | Preprint | uk |
dc.status | Публікується вперше | uk |
dc.description.abstracten | Motion blur is a common issue in image processing and video production. It is cru- cial to have a profound pre-processing method to address this obstacle for various image processing applications, e.g., object detection. Many existing state-of-the-art methods are limited to their computational complexity and memory requirements if one wants to use them in practice on mobile devices. This work proposes sev- eral ideas to overcome this issue and optimize existing solutions via architectural changes and graph optimizations. Moreover, we introduced an adapted version of Soft Attention inside skip connections and achieved a PSNR raise of 0.22 (dB) for the selected baseline. Finally, we derived the optimized model for mobile real-time applications without a significant drop in accuracy, e.g., obtaining 31.36 dB PSNR on GoPro (for image deblurring) with 24 FPS on the mobile application. | uk |