Magnetic resonance imaging (MRI) is non-invasive and crucial for clinical diagnosis, but it has long acquisition time and aliasing artifacts. Accelerated imaging techniques can effectively reduce the scanning time of MRI, thereby decreasing the anxiety and discomfort of patients. Vision Transformer (ViT) based methods have greatly improved MRI image reconstruction, but their computational complexity and memory requirements for the self-attention mechanism grow quadratically with image resolution, which limits their use for high resolution images. In addition, the current generative adversarial networks in MRI reconstruction are difficult to train stably. To address these problems, we propose a Local Vision Transformer (LVT) based adversarial Diffusion model (Diff-GAN) for accelerating MRI reconstruction. We employ a generative adversarial network (GAN) as the reverse diffusion model to enable large diffusion steps. In the forward diffusion module, we use a diffusion process to generate Gaussian mixture distribution noise, which mitigates the gradient vanishing issue in GAN training. This network leverages the LVT module with the local self-attention, which can capture high-quality local features and detailed information. We evaluate our method on four datasets: IXI, MICCAI 2013, MRNet and FastMRI, and demonstrate that Diff-GAN can outperform several state-of-the-art GAN-based methods for MRI reconstruction. 

DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction.

Wavelet and nonlinear model based borescope image compression

In Optical Coherence Tomography (OCT), speckle noise significantly hampers image quality, affecting diagnostic accuracy. Current methods, including traditional filtering and deep learning techniques, have limitations in noise reduction and detail preservation. Addressing these challenges, this study introduces a novel denoising algorithm, Block-Matching Steered-Mixture of Experts with Multi-Model Inference and Autoencoder (BM-SMoE-AE). This method combines block-matched implementation of the SMoE algorithm with an enhanced autoencoder architecture, offering efficient speckle noise reduction while retaining critical image details. Our method stands out by providing improved edge definition and reduced processing time. Comparative analysis with existing denoising techniques demonstrates the superior performance of BM-SMoE-AE in maintaining image integrity and enhancing OCT image usability for medical diagnostics.

Denoising OCT images using steered mixture of experts with multi-model inference

During the process of endoscopic examination, there may be specularity in the image caused by insufficient illumination, and the internal surface of the workpiece has a specular reflection or other reasons. It creates difficulties for further image analysis. The existing algorithms have poor generalization for endoscopic images, and some algorithms’ design problems may cause the image's original features to be treated as specularity and processed. Therefore, we propose a specularity suppression algorithm for endoscope images conforming to Weber-Fechner Law using a single image to obtain a better visual image and reduce the impact of the specularity on further image analysis. Make the gray value the operand to suppress the specularity and the region impacted by it. The algorithm can protect the suppressed area when suppressing the specularity. Experimental results show that the proposed algorithm has a better adaptability to suppress the specularity of the endoscopic images, which can retain more pixels unaffected by the specularity. 

A Specularity Suppression Algorithm for Endoscope Image Conforming to Weber-Fechner Law

Compressed sensing magnetic resonance imaging (CS-MRI) has made great progress in speeding up MRI imaging. The existing non-local self-similarity (NSS) prior based CS-MRI models mainly take similar image patches as the processing objects, this patch-level non-local sparse representation method can not make full use of the self-similarity among pixels in the image, so it can not recover the weak edge information in the undersampled MRI image well and there will still be some artifacts. In this paper, a pixel-level non-local method based compressed sensing undersampled MRI image reconstruction method is introduced. First, zero filling is performed on the undersampled k-space data to obtain a full-size 2D signal, and IFFT is performed to obtain a preliminary reconstructed MRI image. Block-matching and row-matching are successively performed on the reconstructed image in turn to obtain similar pixel groups, so as to establish a better sparse representation under the non-local self-similarity (NSS) prior. The separable Haar transform is performed on similar pixel groups, and the hard threshold of the transform coefficients and Wiener filtering can effectively remove the artifacts introduced in the undersampled reconstructed MRI images. The proposed pixel-level non-local iterative thinning model based on compressed sensing theory can ensure the removal of artifacts and better restore the details in the image. The qualitative and quantitative results under different undersampling modes and undersampling rates prove the advantages of the proposed method in subjective visual quality and objective evaluation (peak signal to noise ratio and structure similarity index). The performance of this method is not only superior to the existing traditional CS-MRI methods, but also competitive with the existing deep neural network (DNN) based models. The code will be released at https://github.com/HaoHou-98/PNCS.

PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction

Using borescope equipment to inspect the inside of turbine engines is an important technology to the daily damage detection of aeronautic engine. Because the borescope image that we observe is based upon point light, and the quantum nature of light is not ideal enough, borescope image acquired through charge-coupled device (CCD) is contaminated by white Gaussian noise. Towards this, a kind of spatially adaptive context-based wavelet shrinkage borescope image denoising method was presented. The spatially adaptive wavelet thresholding was selected based on context modeling, which was used in our prior borescope image compression coder to adapt the probability. Each wavelet coefficient was modeled as a Gibbs field distribution. Context modeling was used to estimate the thresholding for each coefficient. This method was based on an overcomplete non-subsampled wavelet representation, which yielded better results than the orthogonal transform. Experimental results show that spatially adaptive wavelet thresholding yields significantly improved visual quality as well as lower mean squared error (MSE) compared to the method of Chang.

Spatially adaptive context-based wavelet shrinkage for borescope image denoising

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Peng Ding

Author Tools

Chat about Author

Papers

PNCS: Pixel-Level Non-Local Method Based Compressed Sensing Undersampled MRI Image Reconstruction

Spatially adaptive context-based wavelet shrinkage for borescope image denoising