Interference reduction using pattern modulation and demodulation for light-coding depth cameras.

Depth extraction systems with multiple light-coding depth cameras (LCDCs) have been widely used in the field of three-dimensional reconstruction in recent years. However, interference among the depth cameras would significantly deteriorate the quality of depth images and thus limit their efficiency in various applications. In this paper, we first establish the linear illumination model of multiple LCDCs to study the property of interference. Since the interference-free pattern is hard to distinguish from the interfered pattern, we present an interference reduction scheme based on pattern modulation and demodulation to address this problem. Projected patterns from the LCDCs are then uniformly modulated using a coefficient matrix. Afterwards, we further put forward an interference-alignment-based solution to demodulate the captured image frames, thereby fast recovering the interference-free pattern of a single LCDC. Depth images can finally be generated based on the recovered interference-free pattern. Experimental results from simulated and real-world examples show that the proposed scheme can effectively reduce the impact of interference and improve the quality of depth images.

[Special decorrelation technique used for DWT-based hyperspectral image compression].

Hyperspectral images are massive data consisting of hundreds of spectral bands and have been used in a large number of applications. With growth of spectral resolution and spatial resolution of hyperspectral data, data size increases rapidly. How to effectively compress hyperspectral image becomes a key problem that affects the development and popularization of hyperspectral image. Recently, DWT-based methods have been proved promising for hyperspectral image. But their performances are restricted because it is difficult for them to efficiently take advantage of the various properties of hyperspectral image. For the traditional wavelet transform, the specific properties of hyperspectral images are basically utilized by corresponding to characteristics of wavelet coefficients. So the present paper proposes a new DWT-based method using decorrelation technique according to the spectral characters of hyperspectral image. Block predictive coding is designed to remove the spectral correlation as well as spatial correlation simultaneously and is applied into the DWT-based method. Firstly, hyperspectral image is divided into several image blocks. The bands in a single block possess high spectral correlation. Afterwards, it is deduced that bands of a single block tend to be proportional in altitudes. Bands prediction, which is done in the range of each block respectively, is designed according to this and others characteristics of hyperspectral images. Finally, reference bands of block prediction and the deviation data obtained after block prediction are compressed by 2D-DWT algorithm and 3D-DWT algorithm respectively. Experiment results indicate that compared with the well known techniques the proposed method can significantly improve SNR and PSNR performance, even to 4.2 dB (compared with AT-3DSPIHT algorithm). And the code efficiency at low bit rates is also competitive.