Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter

This paper presents a method to reduce noise and refine detail features of a scene based on an iteratively reweighted least squares method. The performance of the proposed filter, called the iteratively reweighted least squares filter (IRLSF), was compared with the state-of-the-art filters by checking their ability to recover simulated edge models under various degrees of noise contamination. The results of the simulation comparison show that IRLSF is superior to the other filters in terms of its ability to recover the original edge models. To apply IRLSF to real images of a scene captured by a camera, a procedure composed of corner detection, least squares matching, bilinear resampling, and iteratively reweighted least squares is proposed. The experimental results show that IRLSF produces mean images that are effectively denoised, and that its accuracy is less than one half of grey-level-quantization-unit of test images captured by a commercial camera.

[1]  Narendra Ahuja,et al.  Real-time O(1) bilateral filtering , 2009, CVPR.

[2]  Guangtao Zhai,et al.  Efficient image sensor noise estimation via iterative re-weighted least squares , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[3]  Fatih Porikli,et al.  Constant time O(1) bilateral filtering , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Guillermo Sapiro,et al.  Anisotropic diffusion of multivalued images with applications to color filtering , 1996, IEEE Trans. Image Process..

[5]  Karl-Rudolf Koch,et al.  Parameter estimation and hypothesis testing in linear models , 1988 .

[6]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Christoph Rasche,et al.  Rapid contour detection for image classification , 2017, IET Image Process..

[8]  Suyoung Seo,et al.  Subpixel Line Localization With Normalized Sums of Gradients and Location Linking With Straightness and Omni-Directionality , 2019, IEEE Access.

[10]  Jörg Weule,et al.  Non-Linear Gaussian Filters Performing Edge Preserving Diffusion , 1995, DAGM-Symposium.

[11]  Suyoung Seo.  Estimation of edge displacement against brightness and camera-to-object distance , 2017, IET Image Process..

[12]  Suyoung Seo.  Line-Detection Based on the Sum of Gradient Angle Differences , 2019, Applied Sciences.

[13]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[14]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Frédo Durand,et al.  A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach , 2006, International Journal of Computer Vision.

[16]  Suyoung Seo.  Subpixel Edge Localization Based on Adaptive Weighting of Gradients , 2018, IEEE Transactions on Image Processing.

[17]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[19]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.

[20]  Shiqian Wu,et al.  Weighted Guided Image Filtering , 2016, IEEE Transactions on Image Processing.

[21]  Suyoung Seo.  Edge Modeling by Two Blur Parameters in Varying Contrasts , 2018, IEEE Transactions on Image Processing.