A patch-number and bandwidth adaptive non-local kernel regression algorithm for multiview image denoising

This paper presents an automatic patch number selection method for bandwidth adaptive non-local kernel regression (BA-NLKR) algorithm, which was recently proposed for improving the performance of conventional non-local kernel regression (NLKR) in image processing. Although BA-NLKR addressed the important issue of bandwidth selection, the number of non-local patches, which impacts the integration of local and non-local information, however is chosen empirically. In this paper, we propose a new algorithm for automatic patch number selection based on the intersecting confidence intervals (ICI) rule in order to achieve better performance. Moreover, the proposed patch number and bandwidth adaptive NLKR (PBA-NLKR) is applied to the denoising problem of multiview images. The effectiveness of the proposed algorithm is illustrated by experimental results on denoising for both single-view and multi-view images.

[1]  Thomas S. Huang,et al.  Non-Local Kernel Regression for Image and Video Restoration , 2010, ECCV.

[2]  Shing-Chow Chan,et al.  On Bandwidth Selection in Local Polynomial Regression Analysis and Its Application to Multi-resolution Analysis of Non-uniform Data , 2008, J. Signal Process. Syst..

[3]  Shing-Chow Chan,et al.  On Kernel Selection of Multivariate Local Polynomial Modelling and its Application to Image Smoothing and Reconstruction , 2011, J. Signal Process. Syst..

[4]  Chong Wang,et al.  A new bandwidth adaptive non-local kernel regression algorithm for image/video restoration and its GPU realization , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).

[5]  Jianqing Fan,et al.  Data‐Driven Bandwidth Selection in Local Polynomial Fitting: Variable Bandwidth and Spatial Adaptation , 1995 .

[6]  Truong Q. Nguyen,et al.  Adaptive non-local means for multiview image denoising: Searching for the right patches via a statistical approach , 2013, 2013 IEEE International Conference on Image Processing.

[7]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[8]  Jaakko Astola,et al.  A spatially adaptive nonparametric regression image deblurring , 2005, IEEE Transactions on Image Processing.

[9]  Shing-Chow Chan,et al.  Local Polynomial Modeling and Variable Bandwidth Selection for Time-Varying Linear Systems , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  Peyman Milanfar,et al.  Kernel Regression for Image Processing and Reconstruction , 2007, IEEE Transactions on Image Processing.

[11]  Yeung Sam Hung,et al.  Local Polynomial Modeling of Time-Varying Autoregressive Models With Application to Time–Frequency Analysis of Event-Related EEG , 2011, IEEE Transactions on Biomedical Engineering.

[12]  Michael Elad,et al.  Super-Resolution Without Explicit Subpixel Motion Estimation , 2009, IEEE Transactions on Image Processing.

[13]  Jean-Michel Morel,et al.  Denoising image sequences does not require motion estimation , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..