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Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated...
Aiming at the applications of image fusion with high contrast and texture information, an effective image fusion method based on redundant-lifting non-separable wavelet multi-directional analysis (NSWMDA) and adaptive pulse coupled neural network (PCNN) has been proposed. The original images are firstly decomposed by using the NSWMDA into several subbands to retain texture detail and contrast information,...
Filtering is a key component of many digital image/video processing algorithms. It often requires FIFO to temporarily buffer the pixels data for later usage. The FIFO size is proportional to the length of the filters and input data width, causing large area and power consumption. This paper presents a technique named FIFO with error-reduced data compression (FERDC) to reduce the FIFO size for various...
Short-term traffic flow forecasting plays an important role in the urban traffic control and guidance system. In the paper, the advantages of wavelet transform and artificial neural network are introduced. Focusing on the characteristics of time-variation and uncertainty of urban traffic flow, the paper adopts the combination of wavelet analysis and artificial neural network, establishes the short-term...
To solve the noise pollution in signal detection of parameter identification, a de-noising approach using wavelet transform is proposed. The de-noising method is theoretically analyzed, experimental data which uses wavelet transformation, is compared with the one that uses Butterworth filer for cantilever friction model parameter identification, the results have proved the reliability of the experimental...
In this paper, a method of blind source separation (BSS) is proposed based on wavelet denoising. This method firstly makes wavelet transform (WT) to the observation signals, and then adopts a uniform method which combines signal whitening and maximum information algorithm to obtain the separate signals which approach to the source signals. Simulations show this method could achieve better performance...
This paper introduces the wavelet threshold de-noising process and the principles of traditional GCV threshold to solve the problems about the distortion and noise removal of the images collected in the high-speed train to monitor real-time for preventing foreign body from the track. It puts forward an image de-noising method on fast recursive GCV threshold function after introduced integer wavelet...
This paper proposes a power and area efficient electrocardiogram (ECG) signal processing application specific integrated circuits (ASIC) for wireless body area networks (WBAN). This signal processing ASIC can accurately detect the QRS peak with high frequency noise suppression. The proposed ECG signal processor is implemented in 0.18μm CMOS technology. It occupies only 1.2 mm2 in area and 9μW in power...
In this paper, a miniature low-power Electrocardiogram (ECG) signal processing application specific integrated circuit (ASIC) chip is proposed. This chip provides multiple critical functions for ECG analysis using a systematic wavelet transform algorithm and a novel SRAM-based ASIC architecture, while achieves low cost and high performance. Using 0.18 μm CMOS technology and 1 V power supply, this...
This paper proposes a new image retrieval method using non-separable discrete wavelets (NDWT) and local binary patterns (LBP). Compared with the traditional wavelet, the three high frequency sub-images generated by the non-separable wavelets can extract more information and do not extensively focus on the three special directions any more. Further, local image texture and their occurrence histogram...
Cognitive radio (CR) is viewed as a novel approach for improving the utilization of radio electromagnetic spectrum resource, and spectrum detection is an important step of CR. As the traditional methods are detecting every channel orderly, and it does not satisfy the real-time needs of CR, a detection algorithm based on blind signal separation is proposed in this paper. Through the sensors, the mixed...
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