Image Deconvolution Method Research Articles

Multiframe blind image deconvolution with split Bregman method

In the paper, a multiframe blind image deconvolution method based on total variation and framelet regularizer is proposed. An adapted version of the split Bregman method is proposed to efficiently solve the resulting minimization problems. In each iteration, four sub-problems need to be solved, one of which can be very efficiently and easily solved via fast Fourier transform implementation or closed form solution. Both simulated noisy and blurred frames and real degraded frames are used to verify the effectiveness of the proposed method. Comparative experimental results show that the proposed method can efficiently remove the blur and noises and restore high quality sharp image.

Optimal Hybrid Deconvolution Method for VLBI Images

In this paper, we present a method for deconvolution of VLBI images based on both maximum entropy and compressive sensing concepts. The parameters of hybrid method are set optimally by utilization of particle swarm optimization (PSO) algorithm. The proposed method is also used to recover source image in a simulated VLBI. The capability of the proposed hybrid method in recovering the main information of target images of astronomical object is shown when the initial measurement data has limited quality. The main advantage of using such hybrid methods together with optimization is to take advantages from various methods in an integrated system.

Optimal Hybrid Deconvolution Method for VLBI Images

In this paper, we present a method for deconvolution of VLBI images based on both maximum entropy and compressive sensing concepts. The parameters of hybrid method are set optimally by utilization of particle swarm optimization (PSO) algorithm. The proposed method is also used to recover source image in a simulated VLBI. The capability of the proposed hybrid method in recovering the main information of target images of astronomical object is shown when the initial measurement data has limited quality. The main advantage of using such hybrid methods together with optimization is to take advantages from various methods in an integrated system. Keywords: Very Long Baseline Interferometry, Maximum Entropy Method, Compressive Sensing, Particle Swarm Optimization.

High quality non-blind image deconvolution using the Fields of Experts prior

In this paper, we propose a non-blind image deconvolution method under Bayesian probabilistic framework. A robust prior, i.e., the Fields of Experts (FoE) prior which is learned from natural images is adopted to regularize the latent image. To solve the resulted optimization problem, the Split Bregman algorithm is utilized. We use a data set to verify the effectiveness of the proposed approach. Experimental results show that the restored images are of higher quality than that of some state of the art algorithms.

Generalized TV and Sparse Decomposition of the Ultrasound Image Deconvolution Model Based on Fusion Technology

Ultrasound image deconvolution involves noise reduction and image feature enhancement, denoising need equivalent the low-pass filtering, image feature enhancement is to strengthen the high-frequency parts, these two requirements are often combined together. It is a contradictory requirement that we must be reasonable balance between these two basic requirements. Image deconvolution method of partial differential equation model is the method based on diffusion theory, and sparse decomposition deconvolution is image representation-based method. The mechanisms of these two methods are not the same, effect of these two methods own characteristics. In contourlet transform domain, we combine the strengths of the two deconvolution method together by image fusion, and introduce the entropy of local orientation energy ratio into fusion decision-making, make a different treatment according to the actual situation on the low-frequency part of the coefficients and the high-frequency part of the coefficient. As deconvolution process is inevitably blurred image edge information, we fusion the edge gray-scale image information to the deconvolution results in order to compensate the missing edge information. Experiments show that our method is better than the effect separate of using deconvolution method, and restore part of the image edge information.

Sparse Bayesian blind image deconvolution with parameter estimation

In this article, we propose a novel blind image deconvolution method developed within the Bayesian framework. We concentrate on the restoration of blurred photographs taken by commercial cameras to show its effectiveness. The proposed method is based on a non-convex l p quasi norm with 0<p<1 that is used for the image, and a total variation (TV) based prior that is utilized for the blur. Bayesian inference is carried out by utilizing bounds for both the image and blur priors using a majorization-minimization principle. Maximum a posteriori estimates of the unknown image, blur and model parameters are calculated. Experimental results (i.e., restorations of more than 30 blurred photographs) are presented to demonstrate the advantage of the proposed method compared to existing ones.

ABC optimized neural network model for image deblurring with its FPGA implementation

Image deblurring is indispensable to many image processing applications. In this paper, we try to improve radiological images degraded during acquisition and processing. An autoregressive moving average (ARMA) model, used for nonlinearly degraded image deconvolution, is identified using a neural network (NN). The NN training is improved using a novel swarm optimization algorithm called Artificial Bees Colony (ABC), inspired from the foraging intelligence of honey bees. The ABC has the advantage of employing fewer control parameters compared to other swarm optimization algorithms. Both estimated image and blur function are identified through this representation. The optimized ARMA-NN model is then implemented on a Xilinx reconfigurable field-programmable gate array (FPGA) using hardware description language: VHDL. This VHDL code is tested on the rapid prototyping platform named ML505 based on a Virtex5-LXT FPGA chip of Xilinx. Simulation results using some test and real images are presented to sustain the applicability of this approach compared to the standard blind image deconvolution (BID) method that maximizes the likelihood using an iterative process. A statistical comparison is concluded based on performance evaluation using seven recent image quality metrics.

Efficient shift-variant image restoration using deformable filtering (Part I)

In this study, we propose using the least squares optimal deformable filtering approximation as an efficient tool for linear shift variant (SV) filtering, in the context of restoring SV-degraded images. Based on this technique we propose a new formalism for linear SV operators, from which an efficient way to implement the transposed SV-filtering is derived. We also provide a method for implementing an approximation of the regularized inversion of a SV-matrix, under the assumption of having smoothly spatially varying kernels, and enough regularization. Finally, we applied these techniques to implement a SV-version of a recent successful sparsity-based image deconvolution method. A high performance (high speed, high visual quality and low mean squared error, MSE) is demonstrated through several simulation experiments (one of them based on the Hubble telescope PSFs), by comparison to two state-of-the-art methods.

Inspection for Nano-Scale Line-Width by Image Deconvolution Method

Inspection for Nano-Scale Line-Width by Image Deconvolution Method

High-quality non-blind image deconvolution with adaptive regularization

Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF estimation is impossible. The conventional regularization to reduce these artifacts cannot preserve image details in the deconvolved image when PSF estimation error is large, so strong regularization is needed. We propose a non-blind image deconvolution method which preserves image details, while suppressing ringing and noise artifacts by controlling regularization strength according to local characteristics of the image. In addition, the proposed method is performed fast with fast Fourier transforms so that it can be a practical solution to image deblurring problems. From experimental results, we have verified that the proposed method restored the sharp latent image with significantly reduced artifacts and it was performed fast compared to other non-blind image deconvolution methods.

Image Deconvolution With Multi-Stage Convex Relaxation and Its Perceptual Evaluation

This paper proposes a new image deconvolution method using multi-stage convex relaxation, and presents a metric for perceptual evaluation of deconvolution results. Recent work in image deconvolution addresses the deconvolution problem via minimization with non-convex regularization. Since all regularization terms in the objective function are non-convex, this problem can be well modeled and solved by multi-stage convex relaxation. This method, adopted from machine learning, iteratively refines the convex relaxation formulation using concave duality. The newly proposed deconvolution method has outstanding performance in noise removal and artifact control. A new metric, transduced contrast-to-distortion ratio (TCDR), is proposed based on a human vision system (HVS) model that simulates human responses to visual contrasts. It is sensitive to ringing and boundary artifacts, and very efficient to compute. We conduct comprehensive perceptual evaluation of image deconvolution using visual signal-to-noise ratio (VSNR) and TCDR. Experimental results of both synthetic and real data demonstrate that our method indeed improves the visual quality of deconvolution results with low distortions and artifacts.

Example-Driven Manifold Priors for Image Deconvolution

Image restoration methods that exploit prior information about images to be estimated have been extensively studied, typically using the Bayesian framework. In this paper, we consider the role of prior knowledge of the object class in the form of a patch manifold to address the deconvolution problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold prior is implicitly estimated from the given unlabeled data. We show how the patch-manifold prior effectively exploits the available sample class data for regularizing the deblurring problem. Furthermore, we derive a generalized cross-validation (GCV) function to automatically determine the regularization parameter at each iteration without explicitly knowing the noise variance. Extensive experiments show that this method performs better than many competitive image deconvolution methods.

Correcting lateral heat conduction effect in image-based heat flux measurements as an inverse problem

The image deconvolution method is developed, which is coupled with the one-dimensional (1D) analytical inverse method to calculate more accurate heat flux fields by correcting the lateral heat conduction effect in image-based surface temperature measurements. The theoretical foundation is a convolution-type integral equation with a Gaussian filter (kernel) that relates a heat flux field obtained by using the 1D inverse method on a surface to the true heat flux field. The accuracy of this method is evaluated and the standard deviation in the Gaussian filter is determined for different materials through simulations. This method is used to calculate heat flux fields in temperature-sensitive-paint measurements on a 7°-half-angle circular cone at Mach 6 in a short-duration hypersonic wind tunnel. In addition, a simple method is proposed to solve a projection problem associated with image deconvolution for a highly curved developable surface.

High order statistics based blind deconvolution of bi-level images with unknown intensity values

We propose a novel linear blind deconvolution method for bi-level images. The proposed method seeks an optimal point spread function and two parameters that maximize a high order statistics based objective function. Unlike existing minimum entropy deconvolution and least squares minimization methods, the proposed method requires neither unrealistic assumption that the pixel values of a bi-level image are independently identically distributed samples of a random variable nor tuning of regularization parameters.We demonstrate the effectiveness of the proposed method in simulations and experiments.

Infrared image enhancement and human detection erformance measures

The ability to detect and recognise dangerous objects at a safe distance is a very important task in a number of defence, police and security applications. In this paper, we look at ways of increasing the effectiveness of infrared imagery for object recognition through processes such as super-resolution image reconstruction and deconvolution methods. In this paper, we propose two techniques for assessing image quality improvement: operator assessment and edge detection; and report on some initial work recently undertaken.

Super-resolution of Infrared Images: Does it Improve Operator Object Detection Performance?

The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more cer- tainty is important. In this paper, super-resolution image reconstruction and deconvolution methods are explored, with an emphasis on quantifying and understanding human operator detection performance.

Improving Infrared Images for Standoff Object Detection

The ability to detect dangerous objects (such as improvised explosive devices) from a distance is important in security and military environments. Standoff imaging can produce images that have been degraded by atmospheric turbulence, movement, blurring and other factors. The number and size of pixels in the imaging sensor can also contribute to image degradation through under-sampling of the image. Establishing processes that enhance degraded or under-sampled infrared images so that objects of interest can be recognised with more certainty is important. Super-resolution image reconstruction and deconvolution methods are explored, as well as performance improvement measures.

Blind deconvolution of quadratic time-frequency representations of musical signals for reverberation feature extraction.

An acoustic space is uniquely characterized by its reverberant behavior. Due to the complexity of the multiple reflections and diffraction of sound in an enclosure, currently available system identification algorithms cannot effectively estimate the impulse response of a concert hall simply from musical recordings in that space. This paper reports the use of blind image deconvolution methods to construct echo patterns from quadratic time-frequency representations of reverberant recordings of music. First, a quadratic time-frequency analysis is performed to decompose the musical signal into its constituent harmonic components. Quadratic time-frequency analysis methods are known to give enhanced resolution in the time-frequency plane in comparison to conventional Fourier analysis. Reverberant features then appear as blur in the time-frequency plane, which can be estimated by employing the methods of blind deconvolution of this “image” of the sound. The proposed algorithm retrieves both the blur pattern, which corresponds to the reverberation echogram, and the direct acoustic signal. By choosing the time-frequency frame scale and smoothing window, a multi-resolution analysis of the underlying acoustic impulse response is obtained. Various quadratic time-frequency analysis methods are evaluated and their relative performance is reported. The proposed methods are also compared to existing dereverberation algorithms.

Single molecule image deconvolution. I. Standard deviation analysis of immobile fluorescent molecules

Single molecule fluorescence imaging has been a powerful technique in studying individual processes not accessible by bulk, ensemble-averaged measurements [1]. Improvements in image analysis are required for high temporal and spatial precision in the localization of single fluorescent molecules. We present the first thorough standard deviation analysis for point spread functions (PSFs) of single immobile fluorescent molecules. Using this new single molecule image deconvolution (SMID) method, we show that 3D localization of individual molecules with sub-nanometer precision can be achieved. We have derived an expression estimating the standard error of the PSF's standard deviation, incorporating experimental effects of the number of collected photons, finite pixel size, and background noise. The localization precision obtained via this expression is approximately 1.5 times better than the current available methods. The use of SMID to extract subexposure dynamics of mobile molecules will also be discussed.[1]. Wang, Y. M, R. H, Austin, & Cox, E. C. 2006 Physical Review Letters 97, 048302(1-4).

Blind Deconvolution Models Regularized by Fractional Powers of the Laplacian

We present a method for deconvolution of images by means of an inversion of fractional powers of the Gaussian. The main feature of our model is the introduction of a regularizing term which is also a fractional power of the Laplacian. This term allows us to recover higher frequencies. The model is particularly useful to devise an algorithm for blind deconvolution. We will show, analyze and illustrate through examples the performance of this algorithm.