Deep Learning for Robust Super-resolution
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".