Quality Of Restored Image Research Articles

Simultaneous auto-calibration and gradient delays estimation (SAGE) in non-Cartesian parallel MRI using low-rank constraints.

To correct gradient timing delays in non-Cartesian MRI while simultaneously recovering corruption-free auto-calibration data for parallel imaging, without additional calibration scans. The calibration matrix constructed from multi-channel k-space data should be inherently low-rank. This property is used to construct reconstruction kernels or sensitivity maps. Delays between the gradient hardware across different axes and RF receive chain, which are relatively benign in Cartesian MRI (excluding EPI), lead to trajectory deviations and hence data inconsistencies for non-Cartesian trajectories. These in turn lead to higher rank and corrupted calibration information which hampers the reconstruction. Here, a method named Simultaneous Auto-calibration and Gradient delays Estimation (SAGE) is proposed that estimates the actual k-space trajectory while simultaneously recovering the uncorrupted auto-calibration data. This is done by estimating the gradient delays that result in the lowest rank of the calibration matrix. The Gauss-Newton method is used to solve the non-linear problem. The method is validated in simulations using center-out radial, projection reconstruction and spiral trajectories. Feasibility is demonstrated on phantom and in vivo scans with center-out radial and projection reconstruction trajectories. SAGE is able to estimate gradient timing delays with high accuracy at a signal to noise ratio level as low as 5. The method is able to effectively remove artifacts resulting from gradient timing delays and restore image quality in center-out radial, projection reconstruction, and spiral trajectories. The low-rank based method introduced simultaneously estimates gradient timing delays and provides accurate auto-calibration data for improved image quality, without any additional calibration scans.

Single image motion deblurring with reduced ringing effects using variational Bayesian estimation

Image degradation commonly occurs when camera translation or irregular movement causes motion blur. In this paper, we remove the effects of unknown camera shake from an image using variational Bayesian estimation and the statistical characteristics of natural image gradients. We then propose a method to reduce ringing effects for image restoration that uses cyclic boundaries. Finally, we use the Richardson-Lucy algorithm to restore an image. Experimental results demonstrate that the blind restoration algorithm that is proposed in this paper can effectively remove image blurring caused by camera shake. Our algorithm can effectively suppress ringing artifacts and eliminate the effects of noise while protecting both edge and detail information. In general, the proposed motion deblurring method is effective in reducing both noise and ringing effects to enhance the quality of motion-blurred image restoration, and we obtained restored images that were clearer and more reliable.

Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a Speed-Up.

Image restoration methods aim to recover the underlying clean image from corrupted observations. The expected patch log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes the EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems, such as denoising, deblurring, super-resolution, inpainting, and devignetting. To the best of our knowledge, the FEPLL is the first algorithm that can competitively restore a pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation.

Spatial Error Concealment Algorithm Based on Adaptive Edge Threshold and Directional Weight

In order to improve the H.264/AVC compressed video stream error resilience in wireless channel transmission, this paper presents a spatial error concealment algorithm based on adaptive edge threshold and directional weight. Firstly, this algorithm makes use of Sobel gradient operator of image edge detection to detect the edge of adjacent macro blocks; secondly, according to specific information of adjacent macro-block of damaged macro-block, it can set gradient adaptive threshold; thirdly, it makes the direction weighted interpolation to damaged macro-block with the Sobel gradient operator of image edge detection. Experiments show that the image reconstruction quality is greatly improved by using this algorithm, which has higher application value for different video sequence as compared to the traditional spatial error concealment algorithms. This algorithm not only improves the quality of image restoration, but also has higher application value.

Edge-illumination x-ray phase contrast imaging restoration using discrete curvelet regularization transform.

This article considers the problem of recovering edge-illumination x-ray phase contrast (EIXPC) images from a set of potentially Poisson noisy projection measurements. The authors cast a recovery as a sparse regularization problem based on Anscombe multiscale variance stabilizing transform (MS-VST) with fast discrete curvelet transform which was applied to simulated edge-illumination x-ray phase contrast images. For accurate modelling, the noise characteristics of the EIXPCi data are used to determine the relative importance of each projection. Two implementations of curvelet sparse regularization transforms were applied, including the unequally-spaced fast Fourier transform and the wrapping-based transform. The algorithms were evaluated in terms of contrast improvement, quality of image restoration, object perceptibility, and peak signal-to-noise ratio. The methods provide nearly optimal solution without excessive memory and recovery time requirement. The performance of the proposed algorithms is demonstrated through a series of complex numerical geometric and anthropomorphic phantom studies. The results of numerical simulations demonstrate that the discrete curvelet transform with MS-VST is fast and robust, and it can effectively improve image quality, preserve and enhance edges and restore lost information while significantly reducing the noise. Additionally, both sparse sampling and decreasing x-ray tube current (i.e. noisy data) lead to the reduction of radiation dose in the x-ray imaging.

Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p < 0.001). The SRCNN-processed image quality in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases were significantly higher than that in low-density breasts, low-risk BI-RADS assessment groups, and benign cases, respectively (p < 0.01). Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.

Speckle Reduction with Trained Nonlinear Diffusion Filtering

Speckle reduction is a prerequisite for many image processing tasks in synthetic aperture radar images, as well as all coherent images. In recent years, predominant state-of-the-art approaches for despeckling are usually based on nonlocal methods which mainly concentrate on achieving utmost image restoration quality, with relatively low computational efficiency. Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality. To this end, we exploit a newly developed trainable nonlinear reaction diffusion (TNRD) framework which has proven a simple and effective model for various image restoration problems. In the original TNRD applications, the diffusion network is usually derived based on the direct gradient descent scheme. However, this approach will encounter some problem for the task of multiplicative noise reduction exploited in this study. To solve this problem, we employed a new architecture derived from the proximal gradient descent method. Taking into account the speckle noise statistics, the diffusion process for the despeckling task is derived. We then retrain all the model parameters in the presence of speckle noise. Finally, optimized nonlinear diffusion filtering models are obtained, which are specialized for despeckling with various noise levels. Experimental results substantiate that the trained filtering models provide comparable or even better results than state-of-the-art nonlocal approaches. Meanwhile, our proposed model merely contains convolution of linear filters with an image, which offers high-level parallelism on GPUs. As a consequence, for images of size $$512 \times 512$$ , our GPU implementation takes less than 0.1 s to produce state-of-the-art despeckling performance.

A Novel Digital Watermarking Approach for Accurate Authenticati on Using of Integer Wavelet Transform Coefficients

2 Abstract: The prominence of digital image processing domain has been increased from last few decades due to its advanced res earch areas such as medicine, biometrics, military, robotics etc. In this work an important issue has taken as area of research i .e. protecting the privacy information from the unauthenticated users either as accidental or incidental ways. Although tremen dous progress has been made in the past years but still protecting the privacy information is concerned area in the field of secu rity. A novel image authentication scheme for gray scale images are implemented in this work and the process of the embeddin g digital watermark is carried by performing the quantization process on the image. The novel things implemented in the propo sed work is detection of tampered parts of the image and to detect minute modification of an image and to embed the watermar k mid frequency band of a second level DWT transform was used. An approximation of the original image based on LL band w as stored as a recovery mark for restoration of the image. Watermarked image has achieved a good PSNR value of 40 dB comp ared to original cover image. Restored image quality was also very good with a PSNR of more than 35 dB compared to unmodi fied watermarked image even when 25% of the received image is cropped. Finally the simulation results reveals that the propos ed method provides the reliable balance between fidelity of the watermarked image and the quality of the restored image.

Fingerprint Recognition System Using Artificial Neural Network as Feature Extractor: Design and Performance Evaluation

Abstract Performance of modern automated fingerprint recognition systems is heavily influenced by accuracy of their feature extraction algorithm. Nowadays, there are more approaches to fingerprint feature extraction with acceptable results. Problems start to arise in low quality conditions where majority of the traditional methods based on analyzing texture of fingerprint cannot tackle this problem so effectively as artificial neural networks. Many papers have demonstrated uses of neural networks in fingerprint recognition, but there is a little work on using them as Level-2 feature extractors. Our goal was to contribute to this field and develop a novel algorithm employing neural networks as extractors of discriminative Level-2 features commonly used to match fingerprints. In this work, we investigated possibilities of incorporating artificial neural networks into fingerprint recognition process, implemented and documented our own software solution for fingerprint identification based on neural networks whose impact on feature extraction accuracy and overall recognition rate was evaluated. The result of this research is a fully functional software system for fingerprint recognition that consists of fingerprint sensing module using high resolution sensor, image enhancement module responsible for image quality restoration, Level-1 and Level-2 feature extraction module based on neural network, and finally fingerprint matching module using the industry standard BOZORTH3 matching algorithm. For purposes of evaluation we used more fingerprint databases with varying image quality, and the performance of our system was evaluated using FMR/FNMR and ROC indicators. From the obtained results, we may draw conclusions about a very positive impact of neural networks on overall recognition rate, specifically in low quality.

A shearlet-based fast thresholded Landweber algorithm for deconvolution

Image deconvolution is an important problem, which has seen plenty of progress in the last decades. Due to its ill-posedness, a common approach is to formulate the reconstruction as an optimization problem[Formula: see text] regularized by an additional sparsity-enforcing term. This term is often modeled as an [Formula: see text] norm measured in the domain of a suitable signal transform. The resulting optimization problem can be solved by an iterative approach via Landweber iterations with soft thresholding of the transform coefficients. Previous approaches focused on thresholding in the wavelet-domain. In particular, recent work [C. Vonesch and M. Unser, A fast thresholded Landweber algorithm for wavelet-regularized multidimensional deconvolution, IEEE Trans. Image Process. 17(4) (2008) 539–549.] has shown that the use of Shannon wavelets results in particularly efficient reconstruction algorithms. The present paper extends this approach to Shannon shearlets, which we also introduce in this work. We show that for anisotropic blurring filters, such as the motion blur, the novel shearlet-based approach allows for further a improvement in efficiency. In particular, we observe that for such kernels using shearlets instead of wavelets improves the quality of image restoration and SERG, when compared after the same number of iterations.

Improved frame-based estimation of head motion in PET brain imaging.

Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method. The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation. The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions. The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.

PERFORMANCE COMPARISON OF NOISE REDUCTION IN MAMMOGRAM IMAGES

Noise level present in mammogram images highly affects the image analysis and accuracy of classification. Hence removing noise present in mammogram images is an important task. Noise present in the medical images depends on imaging modalities. The dominant noise present in mammogram images are quantum noise. The objective of this work is to study the various filters such as mean, median and wiener filter with different window size using standard benchmark (Digital Database for Screening Mammography) DDSM dataset. Higher the value of the (Peak Signal to Noise Ratio) PSNR, better the image quality of the restored image. The restored image quality of various filters was evaluated with PSNR value. We obtained, the wiener filter with window size 3x3 gives better result for noise reduction in mammogram images.

Adaptive tight frame based multiplicative noise removal.

Sparse approximation has shown to be a significant tool in improving image restoration quality, assuming that the targeted images can be approximately sparse under some transform operators. However, it is impossible for a fixed system to be always optimal for all the images. In this paper, we present an adaptive wavelet tight frame technology for sparse representation of an image with multiplicative noise. The adaptive wavelet tight frame is first learned from the logarithmic transformed given images, and then it is used to recover these images. Compared with the existing non-adaptive wavelet sparse transform methods, the numerical results demonstrate that the proposed adaptive tight frame scheme improves image restoration quality.

Pulsed Coherent Teragraphy: Imaging in the Terahertz Frequency Range

Principles of recording the amplitudes and phases of wave fields in the terahertz (THz) frequency range are considered and methodology and schemes used for imaging in this range are analyzed. Generation of THz radiation by femtosecond optical pulses and registration of THz electric field waveforms allow the methods for holographic recording and image restoration to be developed. Results of the experiments on reconstruction of phase characteristics of THz field by the suggested holographic method are demonstrated and the influence of the experimental parameters on the quality of image restoration in time-resolved THz holography is analyzed.

A Novel Fast Volumetric Light Sheet Microscopy

Fast noninvasive three-dimensional (3D) imaging is crucial for the quantitative understanding of highly dynamic biological processes. Over the last decades, several fluorescence microscopy techniques have been developed in order to provide a faster and deeper imaging of thick biological samples [1]. Within this framework, Light Sheet Fluorescence Microscopy (LSFM) has emerged as a powerful imaging tool for 3D imaging of thick samples ranging from single cells to entire animals [2,3].However, to obtain a 3D reconstruction either sample or microscope parts usually need to be moved limiting the acquisition speed and inducing possible interferences in volume recording. To solve this problem, herein we propose a new light-sheet-based optical scheme that enables fast volumetric recording at unprecedented temporal resolution. 3D imaging speeds up to 100 Volumes per Second (faster than three times the volumetric video rate) have been achieved without sample rotation or translation. Volumetric acquisition speed is limited only by the acquisition frame rate of the CCD camera and by a reasonable signal to noise ratio. Our optical approach allows invariant acquisition along the detection axis avoiding extensive processing and deconvolution methods to restore image quality. We demonstrate imaging performance of the microscope by fast volumetric acquisition of flowing beads and live Paramecium movements.1. Diaspro A. Nanoscopy and Multidimensional Optical Fluorescence Microscopy, Chapman and Hall/CRC, (2010).2. E.H. Stelzer. Light-sheet fluorescence microscopy for quantitative biology. Nat Methods. 12(1):23-6. (2014).3. Huisken J and Stainier DYR. Selective plane illumination microscopy techniques in developmental biology. Development, 136(12):1963-1975, (2009).

DCT based efficient fragile watermarking scheme for image authentication and restoration

Due to rapid development of Internet and computer technology, image authentication and restoration are very essential, especially when it is utilized in forensic science, medical imaging and evidence of court. A quantization and Discrete Cosine Transform(DCT) based self-embedding fragile watermarking scheme with effective image authentication and restoration quality is proposed in this paper. In this scheme, the cover image is divided in size of 2×2 non-overlapping blocks. For each block twelve bits watermark are generated from the five most significant bits (MSBs) of each pixel and are embedded into the three least significant bits (LSBs) of the pixels corresponding to the mapped block. The proposed scheme uses two levels encoding for content restoration bits generation. The restoration is achievable with high PSNR and NCC up to 50 % tampering rate. The experimental results demonstrate that the proposed scheme not only outperforms high quality restoration effectively, but also removes the blocking artifacts and improves the accuracy of tamper localization due to use of very small size blocks.

Imaging properties of extended depth of field microscopy through single-shot focus scanning.

Although the single-shot focus scanning technique (SSFS) has been experimentally demonstrated for extended depth of field (EDOF) imaging, few work has been performed to characterize its imaging properties and limitations. In this paper, based on an analytical model of a SSFS system, we examined the properties of the system response and the restored image quality in relation to the axial position of the object, scan range, and signal-to-noise ratio, and demonstrated the properties via a prototype of 10 × 0.25 NA microscope system. We quantified the full range of the achievable EDOF is equivalent to the focus scan range. We further demonstrated that the restored image quality can be improved by extending the focus scan range by a distance equivalent to twice of the standard DOF. For example, in a focus-scanning microscope with a ± 15 μm standard DOF, a 120 μm focus scan range can obtain a ± 60 μm EDOF, but a 150 μm scan range affords noticeably better EDOF images for the same EDOF range. These results provide guidelines for designing and implementing EDOF systems using SSFS technique.

Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model.

Due to the rapid development of motor vehicle Driver Assistance Systems (DAS), the safety problems associated with automatic driving have become a hot issue in Intelligent Transportation. The traffic sign is one of the most important tools used to reinforce traffic rules. However, traffic sign image degradation based on computer vision is unavoidable during the vehicle movement process. In order to quickly and accurately recognize traffic signs in motion-blurred images in DAS, a new image restoration algorithm based on border deformation detection in the spatial domain is proposed in this paper. The border of a traffic sign is extracted using color information, and then the width of the border is measured in all directions. According to the width measured and the corresponding direction, both the motion direction and scale of the image can be confirmed, and this information can be used to restore the motion-blurred image. Finally, a gray mean grads (GMG) ratio is presented to evaluate the image restoration quality. Compared to the traditional restoration approach which is based on the blind deconvolution method and Lucy-Richardson method, our method can greatly restore motion blurred images and improve the correct recognition rate. Our experiments show that the proposed method is able to restore traffic sign information accurately and efficiently.

Image Restoration Based on Pulse Coupled Neural Network

Image is the unity structure and texture, structure reflects to the contours and the boundaries between different regions in the image, and the texture is a reflection of the details within an area in the image, therefore the optimal restoration effect can not be achieved if structure or texture is considered separately during the process of image restoration. In order to solve the problems above, the corresponding solution proposed in this paper is as follows: first, the structural characteristics of the image to be repaired are obtained by using pulse coupled neural network; then both the textural features and the structural features are taken into consideration simultaneously when it comes to the priority of the image restoration as well as the calculation of the optimal sample block. It can be seen from the experimental results that the algorithm described herein can effectively improve the quality of image restoration.

A robust variational model for positive image deconvolution

In this paper, an iterative method for robust deconvolution with positivity constraints is discussed. It is based on the known variational interpretation of the Richardson-Lucy iterative deconvolution as fixed-point iteration for the minimisation of an information divergence functional under a multiplicative perturbation model. The asymmetric penaliser function involved in this functional is then modified into a robust penaliser, and complemented with a regulariser. The resulting functional gives rise to a fixed point iteration that we call robust and regularised Richardson-Lucy deconvolution. It achieves an image restoration quality comparable to state-of-the-art robust variational deconvolution with a computational efficiency similar to that of the original Richardson-Lucy method. Experiments on synthetic and real-world image data demonstrate the performance of the proposed method.