Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries in Wavelet Domain
This paper proposes a novel hybrid image denoising method based on wavelet transform and sparse and redundant representations model which is called signal-scale wavelet K-SVD algorithm (SWK-SVD). In wavelet domain, mutiscale features of images and sparse prior of wavelet coefficients are achieved in a natural way. This gives us the motivation to build sparse representations in wavelet domain. Using K-SVd algorithm, we obtain adaptive and over-complete dictionaries by learning on image approximation and high-frequency wavelet coefficients respectively. This leads to a state-of-art denoising performance both in PSNR and visual effects with strong noise.
Huibin Li Feng Liu
Department of Information and Computing Science, School of Science Xian Jiaotong University, Xian, 710049, China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
754-758
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）