This paper presents a novel decision-based fuzzy filter based on support vector machines and Dempster-Shafer evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector th...

Noise will be unavoidable during image acquisition process and denosing is an essential step to improve the image quality. Image denoising involves the manipulation of the image data to produce a visually high quality image. Finding efficient image denoising methods is still valid challenge in image processing. Wavelet denoising attempts to remove the noise present in the imagery while preservi...

The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level param...

This paper evaluates performances of an adaptive noise cancelling (ANC) based target detection algorithm on a set of real test data supported by the Defense Evaluation Research Agency (DERA UK) for multi-target wideband active sonar echolocation system. The hybrid algorithm proposed is a combination of an adaptive ANC neuro-fuzzy scheme in the first instance and followed by an iterative optimum...

In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, proof-of-concept application 4D diffusion-weighted MRI (dMRI). the imaging landscape, is a key area of research for developing biomarkers that are both sensitive and specific underlying tissue microstructure. However, high-dimensio...

3D meshes are widely employed to represent geometry structure of shapes. Due limitation scanning sensor precision and other issues, inevitably affected by noise, which hampers the subsequent applications. Convolultional neural networks (CNNs) achieve great success in image processing tasks, including 2D denoising, have been proven own capacity modeling complex features at different scales, is a...

In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability significantly enhance energy- and spectrum-efficiency. To this end, it is crucial adjust the phases of elements IRS, most research works focus on how optimize/quantize phase different optimization objectives. particular, quantized shift (QPS) assumed be available at wh...

Introduction MR venography (MRv) has an important role in diagnosis of venous diseases. Unlike x-ray or CT venography, MRv produces venographic images without any ionizing radiation. Obtaining venographic image with low noise and high contrast is clinically important for diagnosis of vascular diseases based on MRv. It has been reported that the best venography can be acquired at TE=28ms for vei...

Arterial spin labeling perfusion MRI is a noninvasive technique for measuring quantitative cerebral blood flow (CBF), but the measurement is subject to a low signal-to-noise-ratio(SNR). Various post-processing methods have been proposed to denoise ASL MRI but only provide moderate improvement. Deep learning (DL) is an emerging technique that can learn the most representative signal from data wi...

In a recent work [I], the authors introduced a discrete uniwr.sa1 denoiser (DUDE) for recovering a signal with finite-valued components corrupted by finite-valued, nncorrelated noise. The DUDE is asymptotically optimal and universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean signal, the same performance as the best denoiser that d...