One-paper-one-short-contribution-summary of all latest image/burst/video Denoising papers with code & citation published in top conference and journal. - oneTaken/Awesome-Denoise
Legrand, H., Thiery, J.M., Boubekeur, T.: Filtered quadrics for high-speed geometry smoothing and clustering. Comput. Graph. Forum 38(1), 663–677 (2019) Article Google Scholar Wang, C., Liu, Z., Liu, L.: Feature-preserving Mumford-Shah mesh processing via nonsmooth nonconvex reg...
. Cells were clustered on a cell by protein Euclidean distance matrix of dsb normalized values not including isotype control proteins as described above. UMAP was run withn_neighborsparameter = 40 andmin_distparameter = 0.4. Cluster labels reflect graph-based clustering in Seurat with ...
which represents one of the first applications of deep learning to scRNA-seq data. We demonstrate that denoising scRNA-seq data can remove technical variation improving five possible downstream analyses, namely clustering, time course modeling, differential expression, protein-RNA co-expression and pseudo...
Cardiac health of the human heart is an intriguing issue for many decades as cardiovascular diseases (CVDs) are the leading cause of death worldwide. Electrocardiogram (ECG) signal is a powerful complete non-invasive tool for analyzing cardiac health. ECG signal is the primary choice of various ...
We further arrange KNNs of the point in a counterclockwise order using local PCA. Feature detection by bi-tensor voting The tensor voting is a fundamental tool in geometry processing for accurately detecting features on high-quality meshes [25], [26]. Recently, it was extended to point clouds...
Within each patch we randomly select N pixels, using stratified sampling to avoid clustering. We then mask these pixels and use the original noisy input val- ues as targets at their position (see Figure 3). Further details on the masking scheme can be found in the supple...
This connectivity analysis revealed none of the “resting state networks” typically observed in the literature; hierarchical tree clustering revealed instead a functional organisation of brain regions that closely resembled cortical anatomy and showed strong links between homologous areas across hemispheres (...
Testing clustering algorithms based on the results of UMAP. Simple partitioning algorithms: K-Means and Gaussian Mixture Model (GMM); Hierarchical clustering: Agglomerative hierarchical clustering and BRICH; Graph-based: Spectral clustering, Louvain, and Leiden; Density-based: OPTICS, DBSCAN, and ...
In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Before starting on the programming exercise, we strongly recommend watc...