Magnetic resonance imaging (MRI) is a revolutionary tool in medical imaging, which plays an important role in clinical diagnosis. Compressive sensing (CS) has shown great potential in significantly reducing the acquisition time of MRI scanning. However, how to improve the reconstruction quality with limited k-space data is still a challenge. MRI images are featured with large area of smooth regions, sharp edges and rich textures. Motivated by these facts, we propose a nonlocal autoregressive model (NAM) for CS MRI reconstruction. Nonlocal similarity between image patches is exploited as a regularization term to constrain the nonlocal feature in MRI images, which is very helpful in preserving edge sharpness. While an autoregressive regularization term is employed to describe the linear correlation between neighboring pixels, which preserves more spatial details. Different from previous work, we reconstruct an MRI image patch utilizing correlations both among patches and among neighboring pixels. Extensive experimental results demonstrate that our method outperforms mainstream methods in MRI reconstruction in terms of both subjective quality and objective quality.
Block truncation coding (BTC) is a fast image compression technique applied in spatial domain. Traditional BTC and its variants mainly focus on reducing computational complexity for low bit rate compression, at the cost of lower quality of decoded images, especially for images with rich texture. To solve this problem, in this paper, a quadtree-based block truncation coding algorithm combined with adaptive bit plane transmission is proposed. First, the direction of edge in each block is detected using Sobel operator. For the block with minimal size, adaptive bit plane is utilized to optimize the BTC, which depends on its MSE loss encoded by absolute moment block truncation coding (AMBTC). Extensive experimental results show that our method gains 0.85 dB PSNR on average compare to some other state-of-the-art BTC variants. So it is desirable for real time image compression applications.
In order to achieve the simulation of elaborate stroke trajectories in Chinese calligraphy, this paper puts forward the innovative researching on writing momentum in the field of non-photorealistic rendering in the first time. Through the analysis of using pen in Chinese calligraphy, the writing momentum is divided into three parts: the center, the side and the back of writing brush by the judgment of the angle of brush holder. We design an algorithm for dynamic outputting writing rendering based on brush model. According to monitoring parameters such as the direction, position and normalized pressure of using pen, we calculate parameters like the footprint direction, the shape, size and nib bending after writing. The algorithm can also judge the dynamic writing trend of stroke trajectories, even automatic generate stroke trajectories by the algorithm forecasted. We achieve a more delicate rendering of Chinese calligraphy to enhance the user's operating results. And we finish the unique writing effect separated the Chinese calligraphy form other general writing results, which greatly enhances the Chinese calligraphy simulation. So that people who lack of writing skills can easily draw a beautiful charm font.
The goal of sign language recognition (SLR) is to translate the sign language into text, and provide a convenient tool for the communication between the deaf-mute and the ordinary. In this paper, we formulate an appropriate model based on convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) network, in order to accomplish the continuous recognition work. With the strong ability of CNN, the information of pictures captured from Chinese sign language (CSL) videos can be learned and transformed into vector. Since the video can be regarded as an ordered sequence of frames, LSTM model is employed to connect with the fully-connected layer of CNN. As a recurrent neural network (RNN), it is suitable for sequence learning tasks with the capability of recognizing patterns defined by temporal distance. Compared with traditional RNN, LSTM has performed better on storing and accessing information. We evaluate this method on our self-built dataset including 40 daily vocabularies. The experimental results show that the recognition method with CNN-LSTM can achieve a high recognition rate with small training sets, which will meet the needs of real-time SLR system.