Image Restoration Filter Research Articles

Estimating Parameters of Optimal Average and Adaptive Wiener Filters for Image Restoration with Sequential Gaussian Simulation

Filtering additive white Gaussian noise in images using the best linear unbiased estimator (BLUE) is technically sound in a sense that it is an optimal average filter derived from the statistical estimation theory. The BLUE filter mask has the theoretical advantage in that its shape and its size are formulated in terms of the image signals and associated noise components. However, like many other noise filtering problems, prior knowledge about the additive noise needs to be available, which is often obtained using training data. This paper presents the sequential Gaussian simulation in geostatistics for measuring signal and noise variances in images without the need of training data for the BLUE filter implementation. The simulated signal variance and the BLUE average can be further used as parameters of the adaptive Wiener filter for image restoration.

Spectral CT Image Restoration via an Average Image-Induced Nonlocal Means Filter.

Spectral computed tomography (SCT) images reconstructed by an analytical approach often suffer from a poor signal-to-noise ratio and strong streak artifacts when sufficient photon counts are not available in SCT imaging. In reducing noise-induced artifacts in SCT images, in this study, we propose an average image-induced nonlocal means (aviNLM) filter for each energy-specific image restoration. Methods: The present aviNLM algorithm exploits redundant information in the whole energy domain. Specifically, the proposed aviNLM algorithm yields the restored results by performing a nonlocal weighted average operation on the noisy energy-specific images with the nonlocal weight matrix between the target and prior images, in which the prior image is generated from all of the images reconstructed in each energy bin. Results:Qualitative and quantitative studies are conducted to evaluate the aviNLM filter by using the data of digital phantom, physical phantom, and clinical patient data acquired from the energy-resolved and -integrated detectors, respectively. Experimental results show that the present aviNLM filter can achieve promising results for SCT image restoration in terms of noise-induced artifact suppression, cross profile, and contrast-to-noise ratio and material decomposition assessment.Conclusion and Significance:The present aviNLM algorithm has useful potential for radiation dose reduction by lowering the mAs in SCT imaging, and it may be useful for some other clinical applications, such as in myocardial perfusion imaging and radiotherapy.

UHD TV image enhancement using example-based spatially adaptive image restoration filter

This paper presents a novel image restoration algorithm using examples and truncated constrained least squares (TCLS) filter for ultra-high definition (UHD) television systems. The proposed approach consists of three steps: (i) generation of the patch dictionary using multiple-step image blurring, (ii) selection of the optimum patch based on the orientation and the amount of blurring, and (iii) combination of the selected patch in the dictionary and its filtered version by the TCLS restoration filter for reducing the patch mismatch error. In the proposed algorithm, a complicated point-spread-function (PSF) estimation process is replaced with the generation of multiple, differently blurred patches. Furthermore, the patch dictionary is made by orientation-based classification to reduce the time to search the optimum patch. Experimental results show that the proposed algorithm can restore more natural images with less synthetic artifacts than existing methods. The proposed method provides a significantly improved restoration performance over existing methods in the sense of both subjective and objective measures including peak-to-peak signal-to-noise ratio (PSNR) and structural similarity measure (SSIM).

A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel’s spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.

Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration

This paper presents a Decision-Based cDNA Microarray Image filter for the removal of impulse noises for complementary deoxyribonucleic acid (cDNA) microarray images. It employs Fuzzy Logic (FL) technique to design a noise detector optimized by Particle Swarm Optimization (PSO), and utilizes center weighted vector median (CWVM) filter for restoring. For convenience, it is called the DBAMI filter hereafter. The proposed filter first employs the FL technique to detect each vector-valued pixel of a cDNA microarray image. If it is considered as noise-corrupted, the DBAMI filter restores it with the CWVM filter. Otherwise, it remains unchanged. Experimental results demonstrate that the DBAMI filter outperforms the existing well-known filters in restoration of cDNA microarray images under considerations here.

A Model-Based Echo Filter for MR Image Restoration with Partial-Echo Acquisition

A Model-Based Echo Filter for MR Image Restoration with Partial-Echo Acquisition

Fast Transforms in Image Processing: Compression, Restoration, and Resampling

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

An Efficient Adaptive Weighted Switching Median Filter for Removing High Density Impulse Noise

Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.

Minimum Statistics-Based Noise Power Estimation for Parametric Image Restoration

This paper describes a method to estimate the noise power using the minimum statistics approach, which was originally proposed for audio processing. The proposed minimum statisticsbased method separates a noisy image into multiple frequency bands using the three-level discrete wavelet transform. By assuming that the output of the high-pass filter contains both signal detail and noise, the proposed algorithm extracts the region of pure noise from the high frequency band using an appropriate threshold. The region of pure noise, which is free from the signal detail part and the DC component, is well suited for minimum statistics condition, where the noise power can be extracted easily. The proposed algorithm reduces the computational load significantly through the use of a simple processing architecture without iteration with an estimation accuracy greater than 90% for strong noise at 0 to 40dB SNR of the input image. Furthermore, the well restored image can be obtained using the estimated noise power information in parametric image restoration algorithms, such as the classical parametric Wiener or ForWaRD image restoration filters. The experimental results show that the proposed algorithm can estimate the noise power accurately, and is particularly suitable for fast, low-cost image restoration or enhancement applications.

Improvement of Bit Error Rate in Holographic Data Storage Using the Extended High-Frequency Enhancement Filter

Optimized image restoration is suggested in angular-multiplexing-page-based holographic data storage. To improve the bit error rate (BER), an extended high frequency enhancement filter is recalculated from the point spread function (PSF) and Gaussian mask as the image restoration filter. Using the extended image restoration filter, the proposed system reduces the number of processing steps compared with the image upscaling method and provides better performance in BER and SNR. Numerical simulations and experiments were performed to verify the proposed method. The proposed system exhibited a marked improvement in BER from 0.02 to 0.002 for a Nyquist factor of 1.1, and from 0.006 to 0 for a Nyquist factor of 1.2. Moreover, more than 3 times faster performance in calculation time was achieved compared with image restoration with PSF upscaling owing to the reductions in the number of system process and calculation load.

Neuro fuzzy and punctual kriging based filter for image restoration

In this paper, we present a hybrid, image restoration approach. The proposed approach combines the geostatistical interpolation of punctual kriging, artificial neural networks (ANNs), and fuzzy logic based approaches. Images degraded with Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that needs kriging. Three fuzzy systems are employed. Both type-I and type-II fuzzy sets in addition with neuro fuzzy classifier (NFC) have been used for the detection of noisy pixels. To avoid edge pixels, a post processing technique is used to check the edge pixel connectivity up to lag 5. If the pixel under consideration is an edge pixel, it is excluded from the fuzzy map and thus not estimated. The concept of punctual kriging is then used to estimate the intensity of a noisy pixel. ANN is employed to minimize the cost function of the kriging based pixel intensity estimation procedure. ANN, in contrast to analytical methodologies, avoids both matrix inversion failure and negative weights problems. Image restoration performance based comparison has been made against adaptive Weiner filter and existing fuzzy kriging approaches. Experimental results using 450 images are used to validate the effectiveness of the proposed approach. Different image quality measures are used to compare the efficacy of the proposed NFC and fuzzy type-II approaches for detecting noisy pixels in conjunction with ANN and kriging based estimation.

Direction based adaptive weighted switching median filter for removing high density impulse noise

Restoration of images corrupted by impulse noises is a very common problem in the image processing. An efficient direction based adaptive weighted switching median filter for restoration of images corrupted by high density impulse noise is proposed in this paper. The filtering process consists of two phases – detection phase followed by a filtering phase. The detection phase in the proposed method uses HEIND algorithm put forward by Fei Duan et al. After detecting noisy pixel positions in the image, filtering algorithm is applied to those detected pixels. All uncorrupted pixels in the image are left unchanged by the filtering algorithm. The filtering algorithm uses uncorrupted pixels in the selected four directions for taking a decision on the filtering window size. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window or directional filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central corrupted pixel in the current filtering window.

Restoration algorithm for image noise removal using double bilateral filtering

We propose a double bilateral (DoBi) filter that consists of a classical bilateral filter and a new bilateral filter for image restoration. Bilateral filtering is a simple, noniterative, and effective denoising filter that smooths images while preserving edges by means of a nonlinear combination of adjacent pixel values. A median-metric weighting function is introduced by incorporating a switching median filter into the similarity function. This median-metric component associated with a spatial function constitute the second bilateral filter, which compensates the classical bilateral filter. Moreover, a parameter automation mechanism is proposed to facilitate the restoration procedure. A wide variety of images contaminated by various degrees of Gaussian, impulse, and mixed noise were used to assess the performance of this new restoration algorithm. Experimental results indicated that the DoBi filter outperformed several state-of-the-art methods in both visual image quality and restored signal quantity.

A Mixed Nonlinear Filter for Image Restoration under AWGN and Impulse Noise Environment

Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which is called mixed noise and it produces a hard damage. In this paper, we proposed noise type classification method and a mixed nonlinear filter for mixed noise suppression. The proposed filtering scheme applies a modified adaptive switching median filter to impulse noise suppression and an efficient nonlinear filer was carried out to remove Gaussian noise. The simulation results based on Matlab show that the proposed method can remove mixed Gaussian and impulse noise efficiently and it can preserve the integrity of edge and keep the detailed information.

Fuzzy similarity measure-based hybrid image filter for color image restoration: multimethodology evolutionary computation

A fuzzy similarity measure-based hybrid image filter (FHF) is proposed for color image restoration in this paper. Operation is carried out in three steps: parameter optimization, hybrid image filter setup, and image restoration. For parameter optimization, a multimethodology evolutionary computation (MMEC) is presented for real-parameter optimization problems. Then, FHF with a fuzzy-based similarity measure is introduced for noise reduction. Finally, a color image is restored with an experience-based construction of FHF which has been optimized via MMEC. Experimental results show the proposed FHF achieves a high peak signal-to-noise ratio and mean structural similarity by effectively reducing Gaussian, impulse, and mixed-noise.

An efficient edge-based bilateral filter for restoring real noisy image

This paper presents an efficient edge-based bilateral filter for real noisy image restoration. By dividing all pixels of a noisy image into edge region or non-edge region, the different strategies and parameters are adopted in the edge-based bilateral filter to balance the conservation of image features and the reduction of noise level. Extensive experimental results are shown that this filter achieves very competitive performance in restoring real noisy images, compared with other state-of-the-art denoising algorithm.

Synthesis of neural pulse interference filters for image restoration

The synthesis of nonlinear pulse interference filters in the form of three-layer and polynomial perceptrons has been considered in this study. Neural filters were used for removing the “salt and pepper“-type pulse interference from half-tone images. It was shown that a three-layer perceptron filter in terms of the filtration accuracy is not inferior to its polynomial analogues and its mathematical model is mush simpler than the Volterra model. Neural and polynomial filters ensure a higher level of pulse interference suppression as compared with median filters.

Acquisition of High Resolution Image by Virtual Stabilization of Observation Satellite

Jitter motion of imaging-type instruments is one of the significant sources of image degradation. The stabilization mechanism is often specified by requiring that image motion be less than some value over certain time interval. In the case of observation satellites, pointing stability of the attitude control system is specified. This is an excellent starting point, but may result in the attitude control system with more capability, hence higher cost and longer development period. Therefore in this paper, a virtual pointing stabilization method is proposed. In this method, the pointing error closely related to the image spatial resolution is numerically compensated at the image processing phase in place of the mechanical stabilization. The pointing error includes both deterministic elements and stochastic ones, and both of them degrade the spatial resolution. In the proposing method, multiple images for a same target are acquired with short exposure time. Then they are composed after compensation of the deterministic pointing error. At last, effects of the stochastic pointing error are compensated by an image restoration filter. Effectiveness of the proposed method is verified through both experiments with a grand test bed and numerical simulations with a complex attitude dynamics model.

Histogram-based fuzzy colour filter for image restoration

A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches).

Improved optimization of soft-partition-weighted-sum filters and their application to image restoration

Soft-partition-weighted-sum (Soft-PWS) filters are a class of spatially adaptive moving-window filters for signal and image restoration. Their performance is shown to be promising. However, optimization of the Soft-PWS filters has received only limited attention. Earlier work focused on a stochastic-gradient method that is computationally prohibitive in many applications. We describe a novel radial basis function interpretation of the Soft-PWS filters and present an efficient optimization procedure. We apply the filters to the problem of noise reduction. The experimental results show that the Soft-PWS filter outperforms the standard partition-weighted-sum filter and the Wiener filter.