Deep Learning and Fuzzy Logic Based Intelligent Technique for the Image Enhancement and Edge Detection Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical imaging is the promising area in digital image processing.Medical images are useful for all types of medical treatment and diagnostics.Medical images are captured through the medical devices, consists some kind of noises and it requires efficient enhancement techniques.Medical imaging also useful in the image segmentation and object detection purposes.Various researcher proposed several types of enhancement techniques and edge detection techniques, but still accuracy and noise are challenge for the enhanced image.So, it is the need of some intelligent techniques to address these issues.In this work we proposed deep learning-based convolution neural network for the image denoising and image enhancement and for the edge detection fuzzy logic-based approach used.The model of DnCNN used here for the image denoising and image enhancement, this model comprises several convolution layers along with input and output layer, this model learns according to the weights and bias.Also, fuzzy logic technique implemented fuzzy inference rules which can give more accurate edges of the image.The result obtained through this hybrid approach is very interesting and effective as compare with previous approaches like histogram-based approach and linear filtering approach.Proposed methods give the promising results as compare with existing methods.All types of simulation performed in MATLAB 2020.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it