Lehua Wu’s research while affiliated with Communication University of China and other places

We apply a method of image quality assessment based on structure similarity (SSIM) to images including regions of interest (ROIs). A gradient-based SSIM model that takes the gradient as the main structural information is used among ROIs, while the classical SSIM model is followed in other regions. Then the final image quality index is evaluated after weights-taxed averaging of indexes in every region. Experimental results show that, considering the importance of ROIs sufficiently, our model is more consistent with perceived visual quality.

The airfield runway usually takes on directional orderliness in the aerial images and remote sensing images, but the traditional edge detection algorithms do not reflect this feature. The directional wavelet transform, which drawed in a directional angle variable thetas, carried out ldquonon-maximum suppressionrdquo and ldquohysteresis thresholdingrdquo on the pixel that through the transform of directional wavelet. It could extract the edges in certain orientation, simultaneously restrain interferential objects in other directions. This approach was employed to detect the edges of the airport runway in the airscape and remote sensing images. Experimental results indicate that this method is insensitive to noise, less computability, and the edges detected have good continuity and localization. Therefore, the method is appropriate for processing such kinds of images.

Based on the concept of cognitive radio (CR) and the idea of no-hit zone (NHZ), a novel cognitive frequency hopping code division multiple access (FH-CDMA) system is proposed, and the related FH sequences with NHZ are designed. When sensing spectrum usage from primary users, the cognitive FH-CDMA system employing NHZ FH sequences can flexibly change hopping frequencies to avoid interference to primary users while the whole FH-CDMA network can continue to be kept orthogonal without multiple access interference (MAI).

In recent years, wavelet-based video coding schemes have attracted considerable attention in the image coding community. In this paper, a new algorithm of three-dimensional wavelet transform (3D-WT) for video coding is presented. The main difference between it and the traditional 3D-WT algorithm is temporal decomposition. The proposed algorithm performs the temporal decomposition of a video sequence in a more efficient way, which is on the basis of several groups separated from the original video sequence. The requirements of processing memory for the proposed algorithm is also analyzed and compared with that of the traditional algorithm. Experimental results show that the proposed algorithm exhibits lower memory demands and better performance of video coding than the traditional 3D-WT algorithm

Video denoising using spatio-temporal information is considered to be more efficient compared with the use of just spatio information. In this paper, a new scheme for video denoising based on 3D wavelet transform is proposed. The input sequence is firstly decomposed into several high frequency frames and one low frequency frame by temporal decomposition. Then, an efficient adaptive threshold is introduced for the low frequency frame denoising while a simple universal threshold for the high frequency ones denosing. Experimental results show that the proposed scheme removes noise more significantly than those algorithms like SureShrink, Winener Filteering and MMSE estimation, which are used in 2D wavelet domain for video denoising without 3D-WT.

A novel multi-scale segmentation scheme based on color clustering and wavelet transform is proposed in this paper. Firstly, a color clustering method is performed to segment images at a coarse scale. Secondly, the high frequency wavelet coefficients and their statistics are used to perform classification of image blocks at fine scales. Post-processing will refine the boundaries of foreground and eventually extract the objects. This novel method is applicable to ordinary images and has achieved high accuracy when tested on images with various background. Experiments show that the new algorithm provides high accuracy and the computer cost is quite short.

In this paper, the classical algorithm of 3D wavelet transform is improved and a new video coding based on the improved algorithm and motion compensation is proposed. The main difference between the classical algorithm and the new algorithm is the temporal decomposition by wavelet transform. With the new algorithm, video sequence is firstly separated into two groups according to the parity of each frame. Then the improved algorithm for temporal decomposition is applied to each group separately which resulting several high frequency frames and one low frequency frame in each group. After that, the motion compensation is only performed in the two low frequency frames in order to eliminate the interframe redundancies. Experiments results show that the proposed scheme has better coding performance compared with other schemes based on 3D-WT.

Wavelet multiresolution decomposition is an effective method to remove noise from signals. The key of removing noise in non-stationary signals is to select suitable wavelets and thresholds for wavelet decomposition coefficients of each level. In this paper, we propose a new method to choose thresholds. The 3σ-rule applied in electronic measurement to remove large errors, which can effectively reduce noises while reserve the saltations of signal, is an excellent method of choosing thresholds.