Dalong Li

Blind Image Deconvolution Through Support Vector Regression

This letter introduces a new algorithm for the restoration of a noisy blurred image based on the support vector regression (SVR). Experiments show that the performance of the SVR is very robust in blind image deconvolution where the types of blurs, point spread function (PSF) support, and noise level are all unknown

Atmospheric Turbulence-Degraded Image Restoration Using Principal Components Analysis

Our earlier work revealed a connection between blind image deconvolution and principal components analysis (PCA). In this letter, we explicitly formulate multichannel and single-channel blind image deconvolution as a PCA problem. Although PCA is derived from blur models that do not contain additive noise, it can be justified on both theoretical and experimental grounds that the PCA-based restoration algorithm is actually robust to the presence of white noise. The algorithm is applied to the restoration of atmospheric turbulence-degraded imagery and compared to an adaptive Lucy-Richardson maximum-likelihood algorithm on both real and simulated atmospheric turbulence blurred images. It is shown that the PCA-based blind image deconvolution runs faster and is more robust to noise.

Blur identification based on kurtosis minimization

In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred image is restored using Wiener filter. We use the kurtosis as a measurement of the quality of the restored image. From the set of the candidate deblurred images, the one with the minimum kurtosis is selected. The proposed technique is tested in a simulated experiment on a variety of blurs including atmospheric turbulence blurs, Gaussian blurs, and out-of-focus blurs. The proposed approach is also tested on real blurred images. Moreover, we test the performance when a wrong blur model is given. Our experiments show that the kurtosis minimization measurements match well with methods that maximize PSNR.

Feature dimensionality reduction for example-based image super-resolution

Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super- resolution. As for other machine learning methods, the training is slow. In this paper, we attempt to address this issue by reducing the feature dimensionality through Principal Component Analysis (PCA). Our single frame supper-resolution experiments show that PCA successfully reduces the feature dimensionality with- out degrading the performance of SVR when the training images and testing images share similarities (i.e. belong to the same category). In fact, in some cases the per- formance in terms of Peak Signal-to-Noise Ratio (PSNR), is even better.

Atmospheric Turbulence Degraded-Image Restoration by Kurtosis Minimization

Atmospheric turbulence is caused by the random fluctuations of the refraction index of the medium. It can lead to blurring in images acquired from a long distance away. Since the degradation is often not completely known, the problem is viewed as blind image deconvolution or blur identification. Our previous work has observed that blurring increases kurtosis and introduced a new blur identification method based on kurtosis minimization (KM). In this letter, this observation has been studied using phase correlation. The KM method is compared with two other signal processing methods. The limitation of the method is also discussed.

Blind image deconvolution using support vector regression

This paper describes an algorithm for the restoration of a noisy blurred image based on support vector regression. The blind image deconvolution was formulated as a machine learning problem. From the training set, the mapping between the noisy blurred image and the original image are learned by support vector regression (SVR). With the acquired mapping, the degraded image can be restored. Our approach was experimentally compared with the adaptive Lucy-Richardson maximum likelihood (ML) algorithm. In terms of ISNR (improvement of signal to noise ratio), SVR outperforms ML in blind deblurring tests in which the types of blurs, point spread function (PSF) support, and noise energy are all unknown.

Single Image Super-Resolution Based on Support Vector Regression

Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR is able to learn a generally applicable model that can super-resolve dissimilar images.

Image Denoising Through Support Vector Regression

In this paper, an example-based image denoising algorithm is introduced. Image denoising is formulated as a regression problem, which is then solved using support vector regression (SVR). Using noisy images as training sets, SVR models are developed. The models can then be used to denoise different images corrupted by random noise at different levels. Initial experiments show that SVR can achieve a higher peak signal-to-noise ratio (PSNR) than the multiple wavelet domain Besov ball projection method on document images.

Example Based Single-frame Image Super-resolution by Support Vector Regression

As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the learning is done. The assumption behind example based methods is the local similarity across seemingly different images. The assumption is illustrated by examples of image coding. Because of the differences in formulating the input/output and the implementation of Support Vector Regression (SVR), it is shown that the proposed approach outperforms the competing SVR method and the kernel regression method in terms of Peak Signal-to-Noise Ratio (PSNR), objective measurements of image quality. Since example based approaches are based on training, in which we know exactly what the output shall be. Therefore, it is proper to objectively measure the performance since the trained model is expected to “correctly” restore the image rather than to enhance the image, e.g. sharpening.

Blind image deconvolution using constrained variance maximization

This paper describes an algorithm based on constrained variance maximization for the restoration of a blurred image. Blurring is a smoothing process by definition. Accordingly, the deblurring filter shall be able to perform as a high pass filter, which increases the variance. Therefore, we formulate a variance maximization object function for the deconvolution filter. Using principal component analysis (PCA), we find the filter maximizing the object function. PCA is more than just a high pass filter; by maximizing the variances, it is able to perform the decorrelation, by which the original image is extracted from the mixture (the blurred image). Our approach was experimentally compared with the adaptive Lucy-Richardson maximum likelihood (ML) algorithm. The comparative results on both synthesized and real blurred images are included.