Chenyang Ge

An Efficient Motion Adaptive De-interlacing and Its VLSI Architecture Design

This paper presents an efficient motion adaptive de-interlacing technique that consists of two main steps, i.e. 4-field extended Gaussian filtering motion detection and adjustable window ELA de-interlacing. Four consecutive interlaced fields are used to detect motion accurately. With a Gaussian filter, the motion detection can eliminate the influence of noise. An adjustable window ELA de-interlacing is adopt to process motion pixels, which can reconstruct image with high quality even in areas with horizontal edge and texture. Experimental results show that the proposed algorithm outperforms previous methods both objectively and subjectively. Based on our method, the high speed VLSI architecture has been designed and implemented. The operating frequency of the chip is 126 MHz that it could process format conversion in real-time for both SDTV and HDTV applications.

Image inpainting strategy for Kinect depth maps

The great advantage of Microsoft Kinect makes the depth acquisition real-time and inexpensive. But the depth maps directly obtained with the Microsoft Kinect device have absent regions and holes caused by optical factors. The noisy depth maps affect lots of complex tasks in computer vision. In order to improve the quality of the depth maps, this paper presents an efficient image inpainting strategy which is based on watershed segmentation and region merging framework of the corresponding color images. The primitive regions produced by watershed transform are merged into lager regions according to color similarity and edge among regions. Finally, mean filter operator to the adjacent pixels is used to fill up missing depth values and deblocking filter is applied for smoothing depth maps.

Depth map generation from geometry and motion

As the demand for 3DTV keep increasing these years, the conversion from exist 2D videos to 3D ones becomes a new area of research. Depth map generation plays a key point in the process. Two most important clues of depth are geometry of the scene and motion vector. This paper presents an algorithm of depth map generation, which intends to get the depth map combines two aspects of information. Compared to the previous work, our method is improved in finding vanishing point, detect motion vectors, and depth map generation.