Sökning: "psnr"

Visar resultat 1 - 5 av 53 uppsatser innehållade ordet psnr.

1. 1. Using Neural Radiance Fields and Gaussian Splatting for 3D reconstruction of aircraft inspections

   Master-uppsats, Lunds universitet/Matematik LTH

   Författare :Roos Eline Bottema; [2024]
   Nyckelord :Technology and Engineering;

   Sammanfattning : The rapid evolution of machine learning techniques has revolutionized computer vision, particularly with the introduction of Neural Radiance Fields (NeRF) and the optimization of 3D Gaussians for rendering novel scene views. These methods, such as NeRF and Gaussian Splatting, have demonstrated success in synthetic data scenarios with consistent lighting and well-captured scenes. LÄS MER

3. 2. Limited angle reconstruction for 2D CT based on machine learning

   Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)

   Författare :Eric Oldgren; Knut Salomonsson; [2023]
   Nyckelord :CT; computed tomography; limited angle; machine learning; limited data; ill posed problem; inverse problem;

   Sammanfattning : The aim of this report is to study how machine learning can be used to reconstruct 2 dimensional computed tomography images from limited angle data. This could be used in a variety of applications where either the space or timeavailable for the CT scan limits the acquired data.In this study, three different types of models are considered. LÄS MER

4. 3. Analyzing the Influence of Synthetic andAugmented Data on Segmentation Model

   Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Författare :Alex Peschel; [2023]
   Nyckelord :Artificial Intelligence; Microorganisms; Segmentation; Synthesizing; Augmentation;

   Sammanfattning : The field of Artificial Intelligence (AI) has experienced unprecedented growth in recent years, thanks to the numerous applications related to speech recognition, natural language processing, and computer vision. However, one of the challenges facing AI is the requirement for large amounts of energy, time, and data to be effective and accurate. LÄS MER

5. 4. Using Generative Adversarial Networks for H&E-to-HER2 Stain Translation in Digital Pathology Images

   Master-uppsats, Linköpings universitet/Institutionen för medicinsk teknik

   Författare :William Tirmén; [2023]
   Nyckelord :Machine learning; Artificial intelligence; Digital pathology; Image processing; Generative adversarial networks; Image-to-image translation;

   Sammanfattning : In digital pathology, hematoxylin & eosin (H&E) is a routine stain which is performed on most clinical cases and it often provides clinicians with sufficient information for diagnosis. However, when making decisions on how to guide breast cancer treatment, immunohistochemical staining of human epidermal growth factor 2 (HER2 staining) is also needed. LÄS MER

7. 5. En jämförelse av Deep Learning-modeller för Image Super-Resolution

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Rafael Bechara; Max Israelsson; [2023]
   Nyckelord :Bachelor’s thesis; Image Super-Resolution; Deep Learning models; EDSR; LapSRN; ESPCN; FSRCNN; resolution enhancement; low-resolution images; quantitative evaluation; RMSE; PSNR; SSIM; Abyssinian cats; dataset; image quality.; Kandidatexamensarbete; Image Super-Resolution; EDSR; LapSRN; ESPCN; FSRCNN; Djupinlärningsmodeller; upplösningsförbättring; lågupplösta bilder; kvantitativ utvärdering; RMSE; PSNR; SSIM; abyssinska katter; datamängd; bildkvalitet.;

   Sammanfattning : Image Super-Resolution (ISR) is a technology that aims to increase image resolution while preserving as much content and detail as possible. In this study, we evaluate four different Deep Learning models (EDSR, LapSRN, ESPCN, and FSRCNN) to determine their effectiveness in increasing the resolution of lowresolution images. LÄS MER