Soongsil University

Beomsu Kim

Min-Cheol Hong

Tien-Daht Nguyen

Huu-Noi Doan

Tuan-Anh Nguyen

Tien-Dat Nguyen

Provided are a hole filling method using estimated spatio-temporal background information and a recording medium and apparatus for performing the same. The present disclosure is providing a hole filling method using estimated spatio-temporal background information, the hole filling method being capable of estimating temporal background information, estimating spatial background information, and merging the temporal background information and the spatial background information to perform a hole filling process, and a recording medium and apparatus for performing the same.

Home filling method using estimated spatio-temporal background information, and recording medium and apparatus for performing the same

Disclosed is a method for hole-filling in 3D models. The method includes extracting static background information from a current frame of an input image and extracting virtual static background information using the static background information, warping a color image and a depth map of the current frame to acquire a virtual image and a virtual depth map, and labeling a hole area formed in the virtual depth map to extract local background information, performing a first hole-filling onto the virtual image and the virtual depth map using a similarity between the virtual static background information and the local background information, and performing a second hole-filling with respect to remaining holes after the first hole-filling in a manner of an exemplar-based in-painting method to which a priority function including a depth term is applied.

Method for hole filling in 3D model, and recording medium and apparatus for performing the same

An image processing system and method is provided. The image processing system includes extracting a first local statistical characteristics from an observed image, generating a low-pass filter according to the first local statistical characteristics to generate a predicted noise image, and extracting second local statistical characteristics from the predicted noise image, extracting a third local statistical characteristics from a predicted original image using the first local statistical characteristics of the observed image and the second local statistical characteristics of the predicted noise image, and setting a flag level indicating a noise level for each pixel of the observed image according to the third local statistical characteristics of the predicted original image to detect noise, and setting a filter coefficient according to the flag level of the detected noise to remove the noise, and restoring the observed image.

Growing community of inventors

Seoul, South Korea

Beomsu Kim