Search Results for author:
Found 7 papers, 2 papers with code
Working memory inspired hierarchical video decomposition with transformative representations
Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e. g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA).
Robust PCA Unrolling Network for Super-resolution Vessel Extraction in X-ray Coronary Angiography
Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved.
Sequential vessel segmentation via deep channel attention network
To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks.
Texture variation adaptive image denoising with nonlocal PCA
For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures.
Registration of Images with Outliers Using Joint Saliency Map
However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima.
Scale Selection of Adaptive Kernel Regression by Joint Saliency Map for Nonrigid Image Registration
JSM guides the local structure matching in nonrigid registration by emphasizing these JSSs' sparse deformation vectors in adaptive kernel regression of hierarchical sparse deformation vectors for iterative dense deformation reconstruction.
Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression
For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images.
