Deblurring Using Regularized Locally Adaptive Kernel Regression (original) (raw)

Regularized kernel regression for image deblurring

2006

The framework of kernel regression [1], a nonparametric estimation method, has been widely used in different guises for solving a variety of image processing problems including denoising and interpolation . In this paper, we extend the use of kernel regression for deblurring applications. Furthermore, we show that many of the popular image reconstruction techniques are special cases of the proposed framework. Simulation results confirm the effectiveness of our proposed methods.

An Adaptive Image Regularization Technique for Image Deblurring

international journal for research in applied science and engineering technology ijraset, 2020

Image processing is important on various fields to achieve various functions. In this paper two classes of regularization strategies to achieve image recovery and reduce noise suppression from Original image in projection-based image deblurring. Landweber iteration leads to a fixed level of regularization, which allows us to achieve fine-granularity control of projection-based iterative deblurring by varying the value. Regularization filters can be gained by probing into their asymptotic behavior-the fixed point of nonexpansive mappings. Different image structures (smooth regions, regular edges and textures) are observed correspond to different fixed points of nonexpansive mappings when the temperature (regularization) parameter varies. Such an analogy motivates us to propose a deterministic annealing based approach toward spatial adaptation in projection-based image deblurring.

A spatially adaptive nonparametric regression image deblurring

IEEE Transactions on Image Processing, 2000

We propose a novel nonparametric regression method for deblurring noisy images. The method is based on the local polynomial approximation (LPA) of the image and the paradigm of intersecting confidence intervals (ICI) that is applied to define the adaptive varying scales (window sizes) of the LPA estimators. The LPA-ICI algorithm is nonlinear and spatially adaptive with respect to smoothness and irregularities of the image corrupted by additive noise. Multiresolution wavelet algorithms produce estimates which are combined from different scale projections. In contrast to them, the proposed ICI algorithm gives a varying scale adaptive estimate defining a single best scale for each pixel. In the new algorithm, the actual filtering is performed in signal domain while frequency domain Fourier transform operations are applied only for calculation of convolutions. The regularized inverse and Wiener inverse filters serve as deblurring operators used jointly with the LPA-design directional kernel filters. Experiments demonstrate the state-of-art performance of the new estimators which visually and quantitatively outperform some of the best existing methods.

Estimation of regularization parameters in multiple-image deblurring

Astronomy & Astrophysics, 2004

We consider the estimation of the regularization parameter for the simultaneous deblurring of multiple noisy images via Tikhonov regularization. We approach the problem in three ways. We first reduce the problem to a single-image deblurring for which the regularization parameter can be estimated through a classic generalized cross-validation (GCV) method. A modification of this function is used for correcting the undersmoothing typical of the original technique. With a second method, we minimize an average least-squares fit to the images and define a new GCV function. In the last approach, we use the classical GCV on a single higher-dimensional image obtained by concatenating all the images into a single vector. With a reliable estimator of the regularization parameter, one can fully exploit the excellent computational characteristics typical of direct deblurring methods, which, especially for large images, makes them competitive with the more flexible but much slower iterative algorithms. The performance of the techniques is analyzed through numerical experiments. We find that under the independent homoscedastic and Gaussian assumptions made on the noise, the three approaches provide almost identical results with the first single image providing the practical advantage that no new software is required and the same image can be used with other deblurring algorithms.

Low-Complexity Regularization Algorithms for Image Deblurring

2016

Image restoration problems deal with images in which information has been degraded by blur or noise. In practice, the blur is usually caused by atmospheric turbulence, motion, camera shake, and several other mechanical or physical processes. In this study, we present two regularization algorithms for the image deblurring problem. We first present a new method based on solving a regularized least-squares (RLS) problem. This method is proposed to find a near-optimal value of the regularization parameter in the RLS problems. Experimental results on the non-blind image deblurring problem are presented. In all experiments, comparisons are made with three benchmark methods. The results demonstrate that the proposed method clearly outperforms the other methods in terms of both the output PSNR and structural similarity, as well as the visual quality of the deblurred images. To reduce the complexity of the proposed algorithm, we propose a technique based on the bootstrap method to estimate t...

Image deblurring and denoising using color priors

2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009

Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an image given a known shift-invariant blur kernel. Our algorithm uses local color statistics derived from the image as a constraint in a unified framework that can be used for deblurring, denoising, and upsampling. A pixel's color is required to be a linear combination of the two most prevalent colors within a neighborhood of the pixel. This two-color prior has two major benefits: it is tuned to the content of the particular image and it serves to decouple edge sharpness from edge strength. Our unified algorithm for deblurring and denoising out-performs previous methods that are specialized for these individual applications. We demonstrate this with both qualitative results and extensive quantitative comparisons that show that we can out-perform previous methods by approximately 1 to 3 DB.

A fast and robust deblurring technique on high noise environment

2013 IEEE International Conference on Image Processing, 2013

To have a unique solution of an ill-posed inverse problem, the usual way is to embed prior information in terms of regularizer or smoothness criterion. In this work, both the inverse mechanism (the relationship of blur and sharp patches) and the smoothness prior are learned simultaneously from the image itself, in multiple scales. We have shown experimentally that the proposed method outperform the existing state-of-the-art techniques on high noise environment and produce comparable result otherwise; moreover, it is almost three times faster than existing ones.

Image Denoising by Adaptive Kernel Regression

Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005., 2005

This paper introduces an extremely robust adaptive denoising filter in the spatial domain. The filter is based on non-parametric statistical estimation methods, and in particular generalizes an adaptive method proposed earlier by Fukunaga [1]. To denoise a pixel, the proposed filter computes a locally adaptive set of weights and window sizes, which can be proven to be optimal in the context of non-parametric estimation using kernels. While we do not report analytical results on the statistical efficiency of the proposed method in this paper, we will discuss its derivation, and experimentally demonstrate its effectiveness against competing techniques at low SNR and on real noisy data.

Blind Image Deblurring based on Kernel Mixture. (arXiv:2101.06241v1 [cs.CV])

arXiv (Cornell University), 2021

Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. A data-driven decision for the number of base kernels to combine makes the structure even more flexible. We apply this approach to a remote sensing problem to recover images from blurry images of satellite. This case study shows the superiority of the proposed method regulating the blur kernel in comparison with state-of-the-art methods that regulates the latent image.

Two-phase kernel estimation for robust motion deblurring

Computer Vision–ECCV 2010, 2010

We discuss a few new motion deblurring problems that are significant to kernel estimation and non-blind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the usefulness of image edges in motion deblurring and a gradient selection process to mitigate their possible adverse effect. We also propose an efficient and high-quality kernel estimation method based on using the spatial prior and the iterative support detection (ISD) kernel refinement, which avoids hard threshold of the kernel elements to enforce sparsity. We employ the TV-1 deconvolution model, solved with a new variable substitution scheme to robustly suppress noise.