Pinned Repositories
3D-Game-Dreamland
aa228
Code for AA228/CS238 Decision Making Under Uncertainty
Classical-Music-Generation-
Colorize-NIR-to-RGB
course project
cs348b-2018-YaoChen
cs348b_assignment
Deep-Learning-Project
faceswap
Deepfakes Software For All
Final-Project---Gaze
Photo-Geolocation-Recognition
Unsupervised-Clustering
EmmaYChen's Repositories
EmmaYChen/3D-Game-Dreamland
EmmaYChen/Photo-Geolocation-Recognition
EmmaYChen/Unsupervised-Clustering
EmmaYChen/aa228
Code for AA228/CS238 Decision Making Under Uncertainty
EmmaYChen/Classical-Music-Generation-
EmmaYChen/Colorize-NIR-to-RGB
course project
EmmaYChen/cs348b-2018-YaoChen
cs348b_assignment
EmmaYChen/Deep-Learning-Project
EmmaYChen/faceswap
Deepfakes Software For All
EmmaYChen/Final-Project---Gaze
EmmaYChen/Rendering-Beetles-Car
EmmaYChen/VR-3Dpainting
EmmaYChen/hdrcnn
HDR image reconstruction from a single exposure using deep CNNs
EmmaYChen/noise-adaptive-switching-non-local-means
Aiming at the removal of salt-and-pepper noise, a noise adaptive switching non-local means denoising algorithm (NASNLM) is proposed in this program. For noise detection, the pixels of image are divided into the noise and the non-noise points. For filtering, four different filtering techniques are adopted: switching filtering, noise adaptive median filtering, edge-perserving filtering and non-local means filtering. Switching filtering can keep the gray-value of non-noise points unchanged. Noise adaptive median filtering can suppress the high-density salt-and-pepper noise. Edge-preserving filtering can preserve more image edges and details. Non-local means filtering can further improve the ability of noise suppression and detail maintenance. Experiments demonstrate that for removal of the high-density salt-and-pepper noise by NASNLM algorithm, a better denoising effect is obtained than other methods.
EmmaYChen/Room-Navigation