Image Denoising Based on Implementing Threshold Techniques in Multi-Resolution Wavelet Domain and Spatial Domain Filters
Bibliographic record
Abstract
Nowadays, a digital image is often easily corrupted due to different forms of noise and complex processes resulting from the acquisition, compression, encoding, transportation, storage, retrieval, etc.All of these factors cause image quality to be distorted and visual information to be lost; in order to overcome this problem, Image denoising techniques are used widely to eliminate the various forms of noise that exist in the deteriorating image while keeping as many fine details and vital signal features as possible in the digital image.The wavelet denoising method aims to remove unwanted noise from a noisy image while preserving its vital features as a result of its ability to divide the degraded image into four sub-bands (sub-images) and operate at the frequencies of each one separately, where acquiring the original image content is vital to achieving reliable performance.This work introduces and implements a new hybrid system to the image denoising caused by Additive White Gaussian Noise (AWGN).The hybrid system is achieved using a combination of Median and Wiener filters as spatial domain filters with two-dimensional stationary and discrete wavelet transform (2D-SWT, 2D-DWT) as a multi-resolution analysis technique by applying 131 wfilters from the wavelet families (haar, db, sym, coif, bior, rbio, dmey, fk) in image processing at three levels of decomposition based on Hard, SureShrink, Bayesian, and Penalized threshold techniques on both high and low frequencies to distinguish and remove noise from affected pixel units and obtain improved results of the noise reduction process to the noisy image.Then, the multi-level 2D inverse wavelet transform (2D-IWT) eliminates noise and completes the image reconstruction by the hybrid denoising technique.Finally, the performance of the hybrid system has been estimated and measured by the peak signal-to-noise ratio (PSNR) value as an image quality metric.Experimental evaluation findings that the results of the proposed approach improved by about 17.5% by comparing them to the results of the related work, as well as enhancement the essence of image quality in terms of better noise reduction and edge preservation instead of using a multi-resolution WT domain or spatial domain filters separately.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".