Digital Image Restoration Research Articles

Image Restoration: Past, Present and Future

Digital Image Restoration is a technique used to recover original image information from degraded images. Digital images lose information because of factors like noise, blurring and camera misfocus. Image restoration plays a significant role in improving the quality of pictures obtained from unmanned aircrafts, poor quality video tape of a crime scene and blurred satellite images. In this paper we review how the field of image restoration has evolved over the years, and how the techniques of image restoration are making inroads into various applications, thus improving users perception. The article also portrays recent advances and patents in the field of image restoration. Keywords: Image deblurring, image restoration, classical approaches to image restoration, adaptive approaches, median filter, image inpainting, inverse filtering, patents on image restoration applications

Improved image quality of low-dose thoracic CT examinations with a new postprocessing software.

In 2008 a phantom study indicated that there is a potential for reducing the CT doses when using a new postprocessing filter. The purpose of this study was to test this new postprocessing filter clinically for low‐dose chest CT examinations, to assess whether the diagnostic performance is the same or improved. A standardized clinical chest CT protocol was used on patients with colorectal cancer. Only mA settings changed between patients according to patient size. One standard and one low‐dose chest protocol were performed for all patients. The low‐dose images were postprocessed with a new software filter, which provides context‐controlled restoration of digital images by using adaptive filters. Three radiologists assessed randomly all the images independently. A total of 24 scan series were evaluated with respect to image quality according to quality criteria from the European guidelines for chest CT using a five‐point scale; 576 details were assessed. Overall mean score is the average score for all details rated for all three readers for all full‐dose series, low‐dose series and low‐dose enhanced series, respectively. The statistical methods used for comparison were paired sampled t‐test and intraclass correlation coefficient. The postprocessing filter improved the diagnostic performance compared to the unenhanced low‐dose images. Mean score for full‐dose, low‐dose and low‐dose enhanced series were 3.8, 3.0 and 3.3, respectively. For all patients the full‐dose series gave higher scores than the low‐dose series. Intraclass correlation coefficients were 0.2, 0.1 and 0.3 for the full‐dose, low‐dose and low‐dose enhanced series, respectively. There is a potential for improving diagnostic performance of low‐dose CT chest examinations using this new postprocessing filter.PACS number: 87.57.C‐, 87.57.Q‐

A new GVF-based image enhancement formulation for use in the presence of mixed noise

This paper is concerned with the introduction of a new gradient vector flow (GVF) field formulation that shows increased robustness in the presence of mixed noise and with its evaluation when included in the development of image enhancement algorithms. In this regard, the main contribution associated with this work resides in the development of an adaptive image enhancement framework that couples the anisotropic diffusion models with the adaptive median filtering that is designed for the restoration of digital images corrupted with mixed noise. To further illustrate the advantages associated with the proposed GVF field formulation, additional experiments are conducted when the proposed strategy is applied in the construction of anisotropic models for texture enhancement.

Parallel deconvolution of large 3D images obtained by confocal laser scanning microscopy

Various deconvolution algorithms are often used for restoration of digital images. Image deconvolution is especially needed for the correction of three-dimensional images obtained by confocal laser scanning microscopy. Such images suffer from distortions, particularly in the Z dimension. As a result, reliable automatic segmentation of these images may be difficult or even impossible. Effective deconvolution algorithms are memory-intensive and time-consuming. In this work, we propose a parallel version of the well-known Richardson-Lucy deconvolution algorithm developed for a system with distributed memory and implemented with the use of Message Passing Interface (MPI). It enables significantly more rapid deconvolution of two-dimensional and three-dimensional images by efficiently splitting the computation across multiple computers. The implementation of this algorithm can be used on professional clusters provided by computing centers as well as on simple networks of ordinary PC machines.

Digital Image Restoration with Nonlinear Smoothing for Circular Motion Blurred Images

Digital Image Restoration with Nonlinear Smoothing for Circular Motion Blurred Images

Applications of the Moore‐Penrose Inverse in Digital Image Restoration

This paper presents a fast computational method that finds application in a broad scientific field such as digital image restoration. The proposed method provides a new approach to the problem of image reconstruction by using the Moore‐Penrose inverse. The resolution of the reconstructed image remains at a very high level but the main advantage of the method was found on the computational load that has been decreased considerably compared to the classic techniques.

Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

A novel image filter based on type-2 fuzzy logic techniques is proposed for detail-preserving restoration of digital images corrupted by impulse noise. The performance of the proposed filter is evaluated for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise filters from the literature. Experimental results show that the proposed filter exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving thin lines, edges, texture, and other useful information within the image.

Reduction in dose from CT examinations of liver lesions with a new postprocessing filter: a ROC phantom study

It is desirable to lower the dose from computed tomography (CT) examinations as much as possible without reducing diagnostic performance. Mathematical postprocessing filters are one tool to achieve dose reduction. To evaluate the possibilities of reducing CT doses from liver examinations using a new postprocessing filter. An anthropomorphic upper-abdomen phantom was used in receiver operating characteristic (ROC) studies of the detectability of liver lesions. A standard abdominal CT protocol was used. Only mA settings were changed; all other scan parameters were constant. The postprocessing filter used was SharpView CT, which provides context-controlled restoration of digital images using adaptive filters. Six readers were given a set of 10 images obtained at five different dose levels, each image with 32 predefined areas to be evaluated on a five-point scale. In total, 1920 areas were evaluated. At each dose level, the readers evaluated five images without enhancement and five images based on postprocessing filters. All images were randomized with respect to dose level. The postprocessing filter improved the diagnostic performance significantly compared to the unenhanced images at all dose levels. Radiation dose for abdominal CT examinations of liver lesions in the range 2-7 mm was reduced by 30% using postprocessing filters, while diagnostic performance of the examination was maintained or even improved. This study indicates great potential for lowering doses for CT examinations of liver lesions using the new postprocessing filter. The software must be fully tested clinically to reliably assess the benefits of this filtration.

VLSI based Design of Multiresolution Median Transform for Digital Image Restoration

VLSI based Design of Multiresolution Median Transform for Digital Image Restoration

Wavelet-based minimal-energy approach to image restoration

The popular mathematical models for digital image restoration, formulated as a minimization problem of certain total energy functionals, give rise to the variational/PDE-based approach to process the input image in the spatial (or physical) domain. In general, the total energy in these models consists of two additive terms, namely: the internal energy for dictating the image quality in terms of image smoothness and image feature preservation, and the external energy for ensuring the output image not to deviate too far from the input image. In formulating the internal energy, a specific energy density function is chosen and applied to the magnitude of the gradient operation for extracting image features to be preserved. In this paper, we replace the gradient operation by some wavelet transform that has better performance in feature extraction and eliminates the need of iterative steps. Thus, the problem of image processing is performed in the wavelet domain instead. By taking advantage of the multi-scale structure of wavelets and the corresponding multi-level singularity detection capability, the proposed approach should facilitate further development of fast and effective algorithms for the variational approach, perhaps even with significant reduction in computational complexity as compared with the traditional approach to digital image restoration.

Digital image restoration by Wiener filter in 2D case

Modern digital technology has made it possible to manipulate multi-dimensional signals with systems that range from simple digital circuits to advanced parallel computers. The goal of this manipulation can be divided into three categories: • Image Processing image in → image out. • Image Analysis image in → measurements out. • Image Understanding image in → high-level description out. Further, we will restrict ourselves to two-dimensional (2D) image processing although most of the concepts and techniques that are to be described can be extended easily to three or more dimensions. The Wiener filter is a solution to the restoration problem based upon the hypothesized use of a linear filter and the minimum mean-square (or rms) error criterion. In the example given below the image a[ m, n] was distorted by a bandpass filter and then white noise was added to achieve an SNR = 30 dB.

Interaction of Fine Particles and Nanoparticles with Red Blood Cells Visualized with Advanced Microscopic Techniques

So far, little is known about the interaction of nanoparticles with lung cells, the entering of nanoparticles, and their transport through the blood stream to other organs. The entering and localization of different nanoparticles consisting of differing materials and of different charges were studied in human red blood cells. As these cells do not have any phagocytic receptors on their surface, and no actinmyosin system, we chose them as a model for nonphagocytic cells to study how nanoparticles penetrate cell membranes. We combined different microscopic techniques to visualize fine and nanoparticles in red blood cells: (I) fluorescent particles were analyzed by laser scanning microscopy combined with digital image restoration, (II) gold particles were analyzed by conventional transmission electron microscopy and energy filtering transmission electron microscopy, and (III) titanium dioxide particles were analyzed by energy filtering transmission electron microscopy. By using these differing microscopic techniques we were able to visualize and detect particles < or = 0.2 microm and nanoparticles in red blood cells. We found that the surface charge and the material of the particles did not influence their entering. These results suggest that particles may penetrate the red blood cell membrane by a still unknown mechanism different from phagocytosis and endocytosis.

On a nonlinear multigrid algorithm with primal relaxation for the image total variation minimisation

Digital image restoration has drawn much attention in the recent years and a lot of research has been done on effective variational partial differential equation models and their theoretical studies. However there remains an urgent need to develop fast and robust iterative solvers, as the underlying problem sizes are large. This paper proposes a fast multigrid method using primal relaxations. The basic primal relaxation is known to get stuck at a ‘local’ non-stationary minimum of the solution, which is usually believed to be ‘non-smooth’. Our idea is to utilize coarse level corrections, overcoming the deadlock of a basic primal relaxation scheme. A further refinement is to allow non-regular coarse levels to correct the solution, which helps to improve the multilevel method. Numerical experiments on both 1D and 2D images are presented.

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise

A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

A median/ANFIS filter for efficient restoration of digital images corrupted by impulse noise

A new operator for removing impulse noise from digital images is presented. The proposed operator is a hybrid filter constructed by combining four center-weighted median filters (CWMF) with a simple adaptive neuro-fuzzy inference system (ANFIS). The internal parameters of the ANFIS are optimized by training. The training is performed by using simple artificial images which can easily be generated by computer. The fundamental advantage of the proposed operator over other operators in the literature is that it efficiently removes impulse noise while at the same time effectively preserves image details and texture. Performance of the proposed operator is tested at various noise densities and for different test images, and also compared with conventional as well as state-of-the-art impulse noise removal operators. Experimental results show that the proposed operator significantly outperforms the other operators and efficiently removes impulse noise from digital images without distorting image details and texture.

A robust structure-adaptive hybrid vector filter for color image restoration

A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.

MO-D-I-609-07: Combined Scatter Subtraction and Digital Restoration of Ho-166 Images for Quantification

Purpose: Ho-166-DOTMP used as targeted radiotherapy to the skeleton has shown great potential for treating multiple myeloma with improved therapeutic index. Potential kidney toxicity has suggested quantification of kidney activity in treatment planning. This has been limited by poor image quality, due to low abundance of imaging photons and relatively high septal penetration of 1.4-MeV photons. Scatter subtraction techniques have been limited because of poor counting statistics after subtraction. We explored the combination of digital restoration and scatter subtraction for Ho-166 image. Method and Materials: A medium-energy collimator was used as a compromise to maximize sensitivity and reduce septal penetration. One photon-window (81-keV) and 2 scatter-windows (lower: 57–73 keV, upper: 89–105 keV) were acquired on a Philips camera with a ¾inch crystal. Images were first subtracted with upper-scatters as energy spectrum from 1.4-MeV photons was flat. Digital restoration was used instead of further subtracting lower scatters. The line-spread function (LSF) was obtained from a subtracted image to determine the modulation transfer function (MTF) at 10-cm depth. Object power spectrum of the Wiener filter was estimated from image power spectrum of Tc-99m-DTPA and Ho-166-DOTMP of the same patient. Results: An upper-scatter subtraction-fraction of 1.0 was confirmed by assessing LSF in air. Subtraction of upper-scatter before Wiener filtering is necessary to remove various image artifacts due to 1.4-MeV photon penetration. The maximum response of the Wiener filter was 2 at 0.08 cycle/pixel and response was decreased to 0.1 at 0.25 cycle/pixel. Image degradation (FWTM) was reduced from 80-mm to 50-mm. Image counting statistics were improved as the total image counts were increased 30% after scatter subtraction and wiener filtering for kidney image 4–6 hour post injection. Conclusion: Combined upper-scatter subtraction and digital restoration improves image quality, allowing quantification of kidney activity with adequate counting statistics to obtain reliable data.

1-dimensional lanthanide halide crystals encapsulated within single-walled carbon nanotubes –a brief review

This brief review describes the crystallisation behaviour of selected lanthanide halides (LnX 3 , with Ln = La, Ce, Gd and X = Cl, I), confined to the nanometre-sized inner cavity of single-walled carbon nanotubes. The resulting LnX 3 @SWNT encapsulation composites were characterised using conventional high-resolution transmission electron microscopy techniques, combined with advanced digital image restoration approaches, enabling the complete structural determination of both the encapsulated crystal fragment and the surrounding nanotube. The studies reveal that some encapsulated 1-dimensional lanthanide halide crystal fragments maintain the overall structure of the respective bulk material, while others exhibit unprecedented crystal structure.

Iterative Lavrentiev regularization for symmetric kernel-driven operator equations: with application to digital image restoration problems

The symmetric kernel-driven operator equations play an important role in mathematical physics, engineering, atmospheric image processing and remote sensing sciences. Such problems are usually ill-posed in the sense that even if a unique solution exists, the solution need not depend continuously on the input data. One common technique to overcome the difficulty is applying the Tikhonov regularization to the symmetric kernel operator equations, which is more generally called the Lavrentiev regularization. It has been shown that the iterative implementation of the Tikhonov regularization can improve the rate of convergence. Therefore in this paper, we study the iterative Lavrentiev regularization method in a similar way when applying it to symmetric kernel problems which appears frequently in applications, say digital image restoration problems. We first prove the convergence property, and then under the widely used Morozov discrepancy principle(MDP), we prove the regularity of the method. Numerical performance for digital image restoration is included to confirm the theory. It seems that the iterated Lavrentiev regularization with the MDP strategy is appropriate for solving symmetric kernel problems.

Detail-Preserving Restoration of Impulse Noise Corrupted Images by a Switching Median Filter Guided by a Simple Neuro-Fuzzy Network

A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter guided by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over other operators is that it offers excellent detail- and texture-preservation performance, while effectively removing noise from the input image. Extensive experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.