Essays about: "Single Image Super-Resolution"

Showing result 1 - 5 of 7 essays containing the words Single Image Super-Resolution.

  1. 1. Ensembles of Single Image Super-Resolution Generative Adversarial Networks

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Victor Castillo Araújo; [2021]
    Keywords : Generative Adversarial Networks; Single Image Super-Resolution; Computer Vision; Convolutional Neural Networks; Ensemble Learning; Generative Adversarial Networks; Superupplösning; Datorseende; Bildanalys; Convolutional neural networks; Ensembler;

    Abstract : Generative Adversarial Networks have been used to obtain state-of-the-art results for low-level computer vision tasks like single image super-resolution, however, they are notoriously difficult to train due to the instability related to the competing minimax framework. Additionally, traditional ensembling mechanisms cannot be effectively applied with these types of networks due to the resources they require at inference time and the complexity of their architectures. READ MORE

  2. 2. Nanoscale detection of insulin granule sub-structures using dSTORM imaging

    University essay from Lunds universitet/Atomfysik; Lunds universitet/Fysiska institutionen

    Author : Yu Hong; [2020]
    Keywords : islet β-cells; dSTORM imaging; Alexa Fluor 647; Physics and Astronomy;

    Abstract : Diabetes mellitus is a global disease, mainly caused by insufficient insulin secre- tion from pancreatic islet β-cells. Insulin is stored in the granules of islet β-cells and released in response to extracellular stimuli e.g. glucose. READ MORE

  3. 3. Compression of Generative Networks for Single Image Super-Resolution

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Jacob Åslund; Anton Dahlin; [2020]
    Keywords : ;

    Abstract : In this research project we have compressed the model size of a generative neural network trained to upscale low resolution images. After first training a large network for this task, we used knowledge distillation to train smaller networks to approximate its output. READ MORE

  4. 4. Super-Resolution for Fast Multi-Contrast Magnetic Resonance Imaging

    University essay from Umeå universitet/Institutionen för fysik

    Author : Erik Nilsson; [2019]
    Keywords : Deep learning; convolutional neural networks; CNN; super-resolution; MRI;

    Abstract : There are many clinical situations where magnetic resonance imaging (MRI) is preferable over other imaging modalities, while the major disadvantage is the relatively long scan time. Due to limited resources, this means that not all patients can be offered an MRI scan, even though it could provide crucial information. READ MORE

  5. 5. Generative adversarial networks for single image super resolution in microscopy images

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Saurabh Gawande; [2018]
    Keywords : Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy; Deep Learning; Generative adversarial networks; Super resolution; High content screening microscopy;

    Abstract : Image Super resolution is a widely-studied problem in computer vision, where the objective is to convert a lowresolution image to a high resolution image. Conventional methods for achieving super-resolution such as image priors, interpolation, sparse coding require a lot of pre/post processing and optimization. READ MORE