Deep Learning for Robust Super-resolution
Notice bibliographique
Résumé
Super Resolution (SR) is a process in which a high-resolution counterpart of an image is reconstructed from its low-resolution sample. Generative Adversarial Networks (GAN), known for their ability of hyper-realistic image generation, demonstrate promising results in performing SR task. High-scale SR, where the super-resolved image is notably larger than low-resolution input, is a challenging but very beneficial task. By employing an SR model, the data can be compressed, more details can be extracted from cheap sensors and cameras, and the noise level will be reduced dramatically. As a result, the high-scale SR model can contribute significantly to face-related tasks, such as identification, face detection, and surveillance systems. Moreover, the resolution of medical scans will be notably increased. So more details can be detected and the early-stage diagnosis will be possible for many diseases such as cancer. Moreover, cheaper and more available scanning devices can be used for accurate abnormality detection. As a result, more lives can be saved because of the enhancement of the accuracy and the availability of scans. \n \n In this thesis, the first multi-scale gradient capsule GAN for SR is proposed. First, this model is trained on CelebA dataset for face SR. The performance of the proposed model is compared with state-of-the-art works and its supremacy in all similarity metrics is demonstrated. A new perceptual similarity index is introduced as well and the proposed architecture outperforms related works in this metric with a notable margin. A robustness test is conducted and the drop in similarity metrics is investigated. As a result, the proposed SR model is not only more accurate but also more robust than the state-of-the-art works. \n \n Since the proposed model is considered as a general SR system, it is also employed for prostate MRI SR. Prostate cancer is a very common disease among adult men. One in seven Canadian men is diagnosed with this cancer in their lifetime. SR can facilitate early diagnosis and potentially save many lives. The proposed model is trained on the Prostate-Diagnosis and PROSTATEx datasets. The proposed model outperformed SRGAN, the state-of-the-art prostate SR model. A new task-specific similarity assessment is introduced as well. A classifier is trained for severe cancer detection and the drop in the accuracy of this model when dealing with super-resolved images is used for evaluating the ability of medical detail reconstruction of the SR models. This proposed SR model is a step towards an efficient and accurate general SR platform.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».