Restoration Filter Research Articles

Application of Spatial Adaptive Filter in the Tilting Mode of Digital Image Restoration

With the development of computer technology and large scale integrated circuit technology, since 1960s, the development of spatial adaptive filter has become one important part of signal processing, its application field has been extended, especially it is used in the digital image processing, and it has become one of the hot research topics in current. This paper presents the recovery method of introducing spatial adaptive filter in the digital image restoration. Firstly, the spatial adaptive filter theory is described, on the basis of this, the hierarchical modeling equilibrium is proposed in the tilting mode digital image restoration, to carry on the analysis of random signal prediction. At the same time, the hierarchical modeling equilibrium will carry out adaptive weight control based on transverse spatial filtering, to reach image restoration for the right cross point. Finally, this paper carries out in-depth analysis for the realization principle of this method, to provide technology support and practical guidance for the research of this field to a certain extent.

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.

Spatial and Frequency Domain Filters for Restoration of Noisy Images

Image restoration is the operation of taking a degraded image and estimating the clean image. Degradation in an image occurs primarily due to blur and noise. Restoration attempts to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Thus restoration techniques are oriented toward modeling the degradation and applying the inverse process in order to recover the original image. This paper starts with basic model of image degradation/restoration process and proceeds towards enlisting commonly occurring blurs and noises. Thereafter, it reviews some spatial and frequency domain filters for restoration of noisy images.

Blind Image Restoration Based on Signal-to-Noise Ratio and Gaussian Point Spread Function Estimation

In order to improve the quality of restored image, a blind image restoration algorithm is proposed, in which both the Signal-to-Noise Ratio (SNR) and the Gaussian Point Spread Function (PSF) of the degraded image are estimated. Firstly, the SNR of the degraded image is estimated through local deviation method. Secondly, the PSF of the degraded image is estimated through error-parameter method. Thirdly, Utilizing the estimated SNR and PSF, high resolution image is restored through Wiener filtering restoration algorithm. Experimental results show that the quality and peak signal-to-noise of the restored image are better around the real value and justify the fact that the SNR an-d PSF estimation plays great important part in blind image restoration.

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.

Video Defogging Based on Adaptive Tolerance

Dark channel prior is a kind of statistics of the haze-free outdoor images, which is widely used in image defogging. But when the image contains a large bright region, such as sky and white object, the prior will cause color distortion in these bright regions because of the underestimated transmission. To solve this problem, a video defogging technology based on adaptive tolerance is presented in this paper, and it is applied to video defogging by combining with the guided filter. First, the transmission of each video frame is estimated according to the dark channel prior, and then it is fast refined by the guided filter for restoration. If a large bright region exists in the video frame, the transmission of these regions will be corrected according to the adaptive tolerance, which avoids the color distortion in the video defogging. For the video of dynamic scenes which are caused by camera motion, each frame is defogged as a single image. But for the video of static scenes whose background is almost invariant, the transmission of the background is estimated and used for the defogging of all the frames instead of estimating the transmission of each frame. In this way, the rate of video defogging is greatly improved. Experimental results show that the algorithm has a strong applicability, and the proposed method can be further used for many applications, such as outdoor surveillance, remote sensing and intelligent vehicles. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1556 Full Text: PDF

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.

Fourier-wavelet restoration in PET/CT brain studies

Our goal is to improve brain PET imaging through the application of a novel, hybrid Fourier-wavelet (WFT) restoration technique. The major limitation of PET studies is a relatively poor resolution in comparison with MRI and CT imaging and there is a need for improved PET imaging. A GE DLS PET/CT 16 slice system was used to acquire the studies. In order to create restoration filters the point source study was performed. The 6-fillable spheres and 3D Hoffman brain phantom studies were acquired and used to test and optimize the restoration approach. The patient data used in the study were acquired in a 3D PET mode, using the standard clinical protocol. Here, we have implemented Fourier-wavelet regularized restoration. In the Fourier domain, the inverse of modulation transfer function was multiplied by a Butterworth low-pass filter, order n=6 and cut-off frequency f=0.35cycles/pixel. In addition, wavelet (Daubechies, order 2) noise suppression was applied by “hard threshold”. Hot spheres and 3D Hoffman brain studies showed that the restoration process not only improves resolution and contrast but also improves quantification in 3D PET/CT imaging. The average contrast increase was 19% and the quantification improved in the range 8−20% depending on sphere size. In the restored images, there was no significant increase in noise when compared with the original images. The clinical studies followed brain phantom findings, i.e., the restored images had better contrast and resolution properties, when compared with the original images. The results of the study demonstrate that the quality and quantification of 3D brain 18F FDG PET images can be significantly improved by Fourier-wavelet (WFT) restoration filtering.

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.

Mathematical model development of super-resolution image Wiener restoration

In super-resolution (SR), a set of degraded low-resolution (LR) images are used to reconstruct a higher-resolution image that suffers from acquisition degradations. One way to boost SR images visual quality is to use restoration filters to remove reconstructed images artifacts. We propose an efficient method to optimally allocate the LR pixels on the high-resolution grid and introduce a mathematical derivation of a stochastic Wiener filter. It relies on the continuous-discrete-continuous model and is constrained by the periodic and nonperiodic interrelationships between the different frequency components of the proposed SR system. We analyze an end-to-end model and formulate the Wiener filter as a function of the parameters associated with the proposed SR system such as image gathering and display response indices, system average signal-to-noise ratio, and inter-subpixel shifts between the LR images. Simulation and experimental results demonstrate that the derived Wiener filter with the optimal allocation of LR images results in sharper reconstruction. When compared with other SR techniques, our approach outperforms them in both quality and computational time.

Experimental results for improving the matrix condition using a hybrid optical system

We present preliminary experimental results for implementing the "blurred trajectories" method on three parallel optics (PO) systems. The "main" system and "auxiliary" optics were simple laboratory graded lenses attached to an iris diaphragm. When applying the blurred trajectories method we first show an improvement in the matrix condition, as the matrix condition number decreased in a range of factors of 3 to 418 relative to the main system. Following that, image restoration by weak regularization was performed so that the system matrix condition dominated the restoration process. It was shown that the restoration results of the PO are better than those of the main system and the auxiliary optics separately. In addition, the quality of the restoration follows the system's matrix condition. The improvement in the matrix condition achieved by the PO system improved the immunity to detection noise. Finally, a comparison to Wiener filtering restoration shows that it is also generally inferior to the proposed method.

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.

Increasing the storage density of a page-based holographic data storage system by image upscaling using the PSF of the Nyquist aperture

We introduce an image upscaling method that reduces bit errors caused by Nyquist apertures. Nyquist apertures used for higher storage densities generate optical aberrations and degrade the quality of the image that is recorded on the medium. Here, to correct the bit errors caused by the Nyquist aperture, an image upscaling method is used to restore the degraded image in the enhanced spatial frequency domain using its point spread function (PSF) as a restoration filter. The proposed method reduces the bit error rate (BER) significantly and hence allows higher storage densities.

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.

Ecological, Economic and Social Challenges, Restoration Filters and Planning for the Unknown

Ecological, Economic and Social Challenges, Restoration Filters and Planning for the Unknown

Local convergence of filter methods for equality constrained non-linear programming

In Gonzaga et al. [A globally convergent filter method for nonlinear programming, SIAM J. Optimiz. 14 (2003), pp. 646–669] we discuss general conditions to ensure global convergence of inexact restoration filter algorithms for non-linear programming. In this article we show how to avoid the Maratos effect by means of a second-order correction. The algorithms are based on feasibility and optimality phases, which can be either independent or not. The optimality phase differs from the original one only when a full Newton step for the tangential minimization of the Lagrangian is efficient but not acceptable by the filter method. In this case a second-order corrector step tries to produce an acceptable point keeping the efficiency of the rejected step. The resulting point is tested by trust region criteria. Under the usual hypotheses, the algorithm inherits the quadratic convergence properties of the feasibility and optimality phases. This article includes a comparison between classical Sequential Quadratic Programming (SQP) and Inexact Restoration (IR) iterations, showing that both methods share the same asymptotic convergence properties.

Unified Loop Filter for Video Compression

This letter proposes a unified loop filter for video coding, which suppresses the quantization noise optimally and improves the objective and subjective qualities of the reconstructed picture simultaneously. The proposed filter unifies nonlinear enhancement filter and linear restoration filter within the classical optimization framework of least mean square error. To adapt to locally diverse quantization error characteristics, classification-based strategy is employed to design unified loop filters with different characteristics, which further improves the capability of picture restoration. Experimental results show that the proposed filter achieves superior objective coding gain and better visual quality improvement around edges and textures, compared with H.264/AVC high profile.

Efficient Median Filter for Restoration of Image and Video Sequences Corrupted by Impulsive Noise

Various switching-based median filters have been proposed in the literature for restoration of extremely corrupted images and video sequences by impulsive noise. Among these, switching median filters with boundary discriminative noise detection (BDND) are very effective and outperform all the previously proposed median-based filters. However, the calculation is very time-consuming. Hence, in this paper, a median filter with an efficient BDND (EBDND) is proposed for denoizing image and video sequences corrupted by impulsive noise. The proposed EBDND uses a faster sorting algorithm both in noise detection and adaptive filtering stages for the restoration of image and video sequences contaminated by impulse noise. For motion estimation of the video sequence, block-matching technique is used. The performance of the proposed EBDND is demonstrated through computer simulations in comparison with the BDND method.

Phase mask selection in wavefront coding systems: A design approach

Abstract A method for optimizing the strength of a parametric phase mask for a wavefront coding imaging system is presented. The method is based on an optimization process that minimizes a proposed merit function. The goal is to achieve modulation transfer function invariance while quantitatively maintaining final image fidelity. A parametric filter that copes with the noise present in the captured images is used to obtain the final images, and this filter is optimized. The whole process results in optimum phase mask strength and optimal parameters for the restoration filter. The results for a particular optical system are presented and tested experimentally in the laboratory. The experimental results show good agreement with the simulations, indicating that the procedure is useful.