基于小波变换和非局部平均的超分辨率图像重建  

Super resolution image reconstruction based on wavelet transform and non-local means

作　　者：叶双清[1] 杨晓梅[1] 

机构地区：[1]四川大学电气信息学院,成都610065

出　　处：《计算机应用》2014年第4期1182-1186,共5页journal of Computer Applications

基　　金：四川大学青年基金资助项目(2011SCU11061)

摘　　要：针对小波域超分辨率方法中重建图像存在的模糊效应,提出一种结合离散小波变换(DWT)、平稳小波变换(SWT)和非局部平均(NLM)的单帧图像重建方法 DSNLM。算法首先对低分辨率图像同时进行DWT和SWT,得到四个子带图像;然后结合对应高频子带图像,直接将原始低频图像作为低频子带,各子带利用NLM滤波处理,得到待重建高分辨率图像的各子带图像;最后,通过离散小波逆变换(IDWT)得到最终的重建高分辨率图像。实验结果和重建视觉效果表明,所提方法与已有的超分辨率方法相比更优,在峰值信噪比(PSNR)、均方差(MSE)和结构相似性度量(SSIM)的评价指标上有显著的提高,对图像去噪、去模糊有效。Combining Discrete Wavelet Transform （DWT）,Stationary Wavelet Transform （SWT） and Non-Local Means （NLM）,a new single-frame Super-Resolution （SR） method named DSNLM was proposed to eliminate the blurring effect in wavelet domain SR image.In DSNLM,the subbands were obtained by applying DWT to low-resolution input image,and SWT was simultaneously applied to obtain high frequency subbands; Then NLM filter was applied to these composite subbands along with the interpolated input image.Finally,Inverse Discrete Wavelet Transform （IDWT） was applied to these subbands to obtain the SR image.The experimental and visual results verify the superiority of the proposed method over the conventional image resolution enhancement techniques with improved Peak Signal-to-Noise Ratio （PSNR）,Mean Squared Error （MSE） and Structural SIMilarity （SSIM）,and it is effective in denoising and blurring.

关 键 词：超分辨率 图像重建 非局部平均 离散小波变换 平稳小波变换 

分 类 号：TP391.413[自动化与计算机技术—计算机应用技术]