Blur identification by residual spectral matching

Line-Detection Based on the Sum of Gradient Angle Differences

This paper presents a method to detect line pixels based on the sum of gradient angle differences (SGAD). The gradient angle differences are calculated by comparing the four pairs of gradients arising from eight neighboring pixels. In addition, a method to classify line pixels into ridges and valleys is proposed. Furthermore, a simple line model is defined for simulation experiments. Experiments are conducted with simulation images generated using the simple line model for three line-detection methods: second-derivatives (SD)-based method, extremity-count (EC)-based method, and proposed method. The results of the simulation experiments show that the proposed method produces more accurate line-detection results than the other methods in terms of the root mean square error when the line width is relatively large. In addition, the experiments conducted with natural images show that the SD- and EC-based methods suffer from bifurcation, fragmentation, and missing pixels. By contrast, for the original and the noise-contaminated versions of the natural images, the proposed SGAD-based line-detection method is affected by such problems to a considerably smaller extent than the other two methods.

Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of finite impulse response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the point spread function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.

Closed-Loop Restoration Approach to Blurry Images Based on Machine Learning and Feedback Optimization

Blind image deconvolution (BID) aims to remove or reduce the degradations that have occurred during the acquisition or processing. It is a challenging ill-posed problem due to a lack of enough information in degraded image for unambiguous recovery of both point spread function (PSF) and clear image. Although recently many powerful algorithms appeared; however, it is still an active research area due to the diversity of degraded images as well as degradations. Closed-loop control systems are characterized with their powerful ability to stabilize the behavior response and overcome external disturbances by designing an effective feedback optimization. In this paper, we employed feedback control to enhance the stability of BID by driving the current estimation quality of PSF to the desired level without manually selecting restoration parameters and using an effective combination of machine learning with feedback optimization. The foremost challenge when designing a feedback structure is to construct or choose a suitable performance metric as a controlled index and a feedback information. Our proposed quality metric is based on the blur assessment of deconvolved patches to identify the best PSF and computing its relative quality. The Kalman filter-based extremum seeking approach is employed to find the optimum value of controlled variable. To find better restoration parameters, learning algorithms, such as multilayer perceptron and bagged decision trees, are used to estimate the generic PSF support size instead of trial and error methods. The problem is modeled as a combination of pattern classification and regression using multiple training features, including noise metrics, blur metrics, and low-level statistics. Multi-objective genetic algorithm is used to find key patches from multiple saliency maps which enhance performance and save extra computation by avoiding ineffectual regions of the image. The proposed scheme is shown to outperform corresponding open-loop schemes, which often fails or needs many assumptions regarding images and thus resulting in sub-optimal results.

No-reference sharpness assessment of camera-shaken images by analysis of spectral structure

The tremendous explosion of image-, video-, and audio-enabled mobile devices, such as tablets and smart-phones in recent years, has led to an associated dramatic increase in the volume of captured and distributed multimedia content. In particular, the number of digital photographs being captured annually is approaching 100 billion in just the U.S. These pictures are increasingly being acquired by inexperienced, casual users under highly diverse conditions leading to a plethora of distortions, including blur induced by camera shake. In order to be able to automatically detect, correct, or cull images impaired by shake-induced blur, it is necessary to develop distortion models specific to and suitable for assessing the sharpness of camera-shaken images. Toward this goal, we have developed a no-reference framework for automatically predicting the perceptual quality of camera-shaken images based on their spectral statistics. Two kinds of features are defined that capture blur induced by camera shake. One is a directional feature, which measures the variation of the image spectrum across orientations. The second feature captures the shape, area, and orientation of the spectral contours of camera shaken images. We demonstrate the performance of an algorithm derived from these features on new and existing databases of images distorted by camera shake.

Motion-blurred star acquisition method of the star tracker under high dynamic conditions

The star tracker is one of the most promising attitude measurement devices used in spacecraft due to its extremely high accuracy. However, high dynamic performance is still one of its constraints. Smearing appears, making it more difficult to distinguish the energy dispersive star point from the noise. An effective star acquisition approach for motion-blurred star image is proposed in this work. The correlation filter and mathematical morphology algorithm is combined to enhance the signal energy and evaluate slowly varying background noise. The star point can be separated from most types of noise in this manner, making extraction and recognition easier. Partial image differentiation is then utilized to obtain the motion parameters from only one image of the star tracker based on the above process. Considering the motion model, the reference window is adopted to perform centroid determination. Star acquisition results of real on-orbit star images and laboratory validation experiments demonstrate that the method described in this work is effective and the dynamic performance of the star tracker could be improved along with more identified stars and guaranteed position accuracy of the star point.

Minimum entropy restoration of star field images

In this correspondence, we present an algorithm for restoration of star field images by incorporating both the minimum mean square error and the maximum varimax criteria. It is assumed that the point spread function of the distortion system can be well approximated by a Gaussian function. Simulated annealing (SA) is used to implement the optimization procedure. Simulation results for both Gaussian and square point spread functions with heavy additive independent white Gaussian noise are provided. Visual evaluation of the results indicate that the proposed algorithm performs better than the noncausal Wiener filtering method.

Facial deblur inference using subspace analysis for recognition of blurred faces

This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.

Combined invariants to similarity transformation and to blur using orthogonal Zernike moments

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better.

Blurred image recognition by Legendre moment invariants

Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments.

Empirical capacity of a recognition channel for single- and multipose object recognition under the constraint of PCA encoding

The ability of practical recognition systems to recognize a large number of objects is constrained by a variety of factors that include choice of a feature extraction technique, quality of images, complexity and variability of underlying objects and of collected data. Given a feature extraction technique generating templates of objects from data and a resolution of the original images, the remaining factors can be attributed to distortions due to a recognition channel. We define the recognition channel as the environment that transforms reference templates of objects in a database into templates submitted for recognition. If templates in an object database are generated to be statistically independent and the noise in a query template is statistically independent of templates in the database, then the abilities of the recognition channel to recognize a large number of object classes can be characterized by a number called recognition capacity. In this paper, we evaluate the empirical recognition capacity of PCA-based object recognition systems. The encoded data (templates) and the additive noise in query templates are modeled to be Gaussian distributed with zero mean and estimated variances. We analyze both the case of a single encoded image and the case of encoded correlated multiple images. For this case, we propose a model that is orientation and elevation angle (pose) dependent. The fit of proposed models is judged using statistical goodness of fit tests. We define recognition rate as the ratio R=log(M)/n, where M is the number of objects to recognize and n is the length of PCA templates. The empirical capacity of PCA-based recognition systems is numerically evaluated. The empirical random coding exponent is also numerically evaluated and plotted as a function of the recognition rate. With these results, given a value of the recognition capacity and the length of templates (assume large), we can predict the number of distinct object classes that can be stored in an object library and be identified with probability of error close to zero.

Blind deconvolution using a variational approach to parameter, image, and blur estimation

Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods.

New automated iris image acquisition method

I propose a new iris image acquisition method based on wide- and narrow-view iris cameras. The narrow-view camera has the functionalities of automatic zooming, focusing, panning, and tilting based on the two-dimensional and three-dimensional eye positions detected from the wide- and narrow-view stereo cameras. By using the wide- and narrow-view iris cameras, I compute the user's gaze position, which is used for aligning the X-Y position of the user's eye, and I use the visible-light illuminator for fake-eye detection.

Blur identification from vector quantizer encoder distortion

Blur identification is a crucial first step in many image restoration techniques. An approach for identifying image blur using vector quantizer encoder distortion is proposed. The blur in an image is identified by choosing from a finite set of candidate blur functions. The method requires a set of training images produced by each of the blur candidates. Each of these sets is used to train a vector quantizer codebook. Given an image degraded by unknown blur, it is first encoded with each of these codebooks. The blur in the image is then estimated by choosing from among the candidates, the one corresponding to the codebook that provides the lowest encoder distortion. Simulations are performed at various bit rates and with different levels of noise. Results show that the method performs well even at a signal-to-noise ratio (SNR) as low as 10 dB.

Comparison of direct blind deconvolution methods for motion-blurred images

Direct methods for restoration of images blurred by motion are analyzed and compared. The term direct means that the considered methods are performed in a one-step fashion without any iterative technique. The blurring point-spread function is assumed to be unknown, and therefore the image restoration process is called blind deconvolution. What is believed to be a new direct method, here called the whitening method, was recently developed. This method and other existing direct methods such as the homomorphic and the cepstral techniques are studied and compared for a variety of motion types. Various criteria such as quality of restoration, sensitivity to noise, and computation requirements are considered. It appears that the recently developed method shows some improvements over other older methods. The research presented here clarifies the differences among the direct methods and offers an experimental basis for choosing which blind deconvolution method to use. In addition, some improvements on the methods are suggested.

Multichannel restoration with limited a priori information

We introduce a method for multichannel restoration of images in which there is severely limited knowledge about the undegraded signal, and possibly the noise. We assume that we know the channel degradations and that there will be a significant noise reduction in a postprocessing stage in which multiple realizations are combined. This post-restoration noise reduction is often performed when working with micrographs of biological macromolecules. The restoration filters are designed to enforce a projection constraint upon the entire system. This projection constraint results in a system that provides an oblique projection of the input signal into the subspace defined by the reconstruction device in a direction orthogonal to a space defined by the channel degradations and the restoration filters. The approach achieves noise reduction without distorting the signal by exploiting the redundancy of the measurements.

Recognition of blurred images by the method of moments

The article is devoted to the feature-based recognition of blurred images acquired by a linear shift-invariant imaging system against an image database. The proposed approach consists of describing images by features that are invariant with respect to blur and recognizing images in the feature space. The PSF identification and image restoration are not required. A set of symmetric blur invariants based on image moments is introduced. A numerical experiment is presented to illustrate the utilization of the invariants for blurred image recognition. Robustness of the features is also briefly discussed.