High-quality images acquired from medical devices can be utilized to aid diagnosis and
detection of various diseases. However, such images can be very expensive to acquire and …

Ensemble models based on deep learning have made significant contributions to the
medical field, particularly in the area of disease prediction. Breast cancer is a highly …

Until today, researchers have introduced a range of methodologies to decrease the noise
effect on medical images. In the proposed approach, an adapted deep convolutional neural …

Denoising computed tomography (CT) medical images is crucial in preserving information
and restoring images contaminated with noise. Standard filters have extensively been used …

Fluorescence in situ hybridization (FISH) is a powerful cytogenetic method used to precisely
detect and localize nucleic acid sequences. This technique is proving to be an invaluable …

Medical image denoising is considered among the most challenging vision tasks. Despite
the real-world implications, existing denoising methods have notable drawbacks as they …

The realm of medical imaging is a critical frontier in precision diagnostics, where the clarity
of the image is paramount. Despite advancements in imaging technology, noise remains a …

Images are frequently disrupted by noise of all kinds, making image restoration very
challenging. There have been many different image denoising models proposed over the …

Recognition systems based on palm veins are gaining increasing attention as they are
highly distinctive and very hard to counterfeit. Most popular systems are based on infrared …

Image denoising is a critical issue in industrial computed tomography (CT) inspection. Most
existing noise reduction algorithms are based on synthetic data, resulting in the loss of fine …