Sökning: "image enhancement"

Visar resultat 1 - 5 av 112 uppsatser innehållade orden image enhancement.

  1. 1. Mutual Enhancement of Environment Recognition and Semantic Segmentation in Indoor Environment

    Master-uppsats,

    Författare :Venkata Vamsi Challa; [2024]
    Nyckelord :Semantic Segmentation; Scene Classification; Environment Recognition; Machine Learning; Deep Learning; Image Classification; Vision Transformers; SAM Segment Anything Model ; Image Segmentation; Contour-aware semantic segmentation;

    Sammanfattning : Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. LÄS MER

  3. 2. ISAR Imaging Enhancement Without High-Resolution Ground Truth

    Master-uppsats, Linköpings universitet/Datorseende

    Författare :Moltas Enåkander; [2023]
    Nyckelord :SAR; SAR Imaging; ISAR; ISAR Imaging; Machine learning; Convolutional neural network; CNN; neural network; Super resolution; Unsupervised learning;

    Sammanfattning : In synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR), an imaging radar emits electromagnetic waves of varying frequencies towards a target and the backscattered waves are collected. By either moving the radar antenna or rotating the target and combining the collected waves, a much longer synthetic aperture can be created. LÄS MER

  4. 3. Quality enhancement of time-resolved computed tomography scans with cycleGAN

    Master-uppsats, Lunds universitet/Synkrotronljusfysik; Lunds universitet/Fysiska institutionen

    Författare :Johannes Stubbe; [2023]
    Nyckelord :carbon fibers; carbon fibres; microfibers; tomography; deep learning; cycleGAN; time-resolved tomography; Physics and Astronomy;

    Sammanfattning : Time-resolved x-ray tomography enables us to dynamically and non-destructively study the interior of a specimen. The obtainable temporal resolution is limited by the x-ray flux and the desired spatial resolution. LÄS MER

  5. 4. Domain Adaptation for Multi-Contrast Image Segmentation in Cardiac Magnetic Resonance Imaging

    Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

    Författare :Thomas Proudhon; [2023]
    Nyckelord :Cardiac Magnetic Resonance Imaging; Deep Learning; Domain Adaptation; Unsupervised Segmentation; Image-to-image Translation;

    Sammanfattning : Accurate segmentation of the ventricles and myocardium on Cardiac Magnetic Resonance (CMR) images is crucial to assess the functioning of the heart or to diagnose patients suffering from myocardial infarction. However, the domain shift existing between the multiple sequences of CMR data prevents a deep learning model trained on a specific contrast to be used on a different sequence. 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