In this study, we delve into the generation of high-resolution images from pre-trained
diffusion models, addressing persistent challenges, such as repetitive patterns and structural …

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation
(AM-FM) methods for discriminating between normal and pathological retinal images. The …

Ultrasonic image segmentation is a difficult problem due to speckle noise, low contrast, and
local changes of intensity. Intensity-based methods do not perform particularly well on …

Deep convolutional neural networks (DCNNs) have recently emerged as a powerful tool to
deliver breakthrough performances in various image analysis and processing applications …

This study introduces the use of multiscale amplitude modulation-frequency modulation (AM-
FM) texture analysis of multiple sclerosis (MS) using magnetic resonance (MR) images from …

In this paper, a novel level set segmentation model integrating the intensity and texture
terms is proposed to segment complicated two-phase nature images. Firstly, an intensity …

Medical image analysis methods require the use of effective representations for
differentiating between lesions, diseased regions, and normal structure. Amplitude …

We develop new multiscale amplitude-modulation frequency-modulation (AM-FM)
demodulation methods for image processing. The approach is based on three basic ideas:(i) …

We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases
based on a fundamental approximation-theoretic characterization of scaling functions—the …

Amplitude-modulation frequency-modulation (AM-FM) decompositions represent images
using spatially-varying sinusoidal waves and their spatially-varying amplitudes. AM-FM …