EmmaYChen

3D-Game-Dreamland

Photo-Geolocation-Recognition

Unsupervised-Clustering

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

Rendering-Beetles-Car

VR-3Dpainting

hdrcnn

HDR image reconstruction from a single exposure using deep CNNs

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.

Room-Navigation