Image Sharpening Research Articles

EFFECTS OF IMAGE PROCESSING UPON RADIOGRAPHIC IMAGE QUALITY

<h3>Background</h3> There is evidence that capture and processing of the digital radiographic images may contain artifacts that could reduce the diagnostic value of dental radiographs. This phenomenon has been reported through various case reports. However, its effect on radiographic image quality has not been studied. <h3>Objective(s)</h3> The purpose of this study was to evaluate the effect of image processing on the quality of dental digital radiographs using the most common type of filter—sharpening. <h3>Study Design</h3> Three imaging modalities (2 complementary metal-oxide-semiconductor [CMOS], 1 photostimulable phosphor [PSP]) were used to image a DDQA imaging phantom (Digital Dental Quality Assurance phantom) and to create a flatfield radiograph. Contrast resolution, spatial resolution, dynamic range, and radiographic noise were assessed for image quality. Overshoot artifact was also assessed. Each image was sharpened and then highly sharpened. Images were compared using Friedman's 2-way nonparametric analysis of variance (ANOVA). <h3>Results</h3> Image sharpening significantly affected spatial resolution and noise. Conversely, dynamic range and contrast resolution were not significantly affected by image sharpening. Overshoot artifact increased with image sharpening. <h3>Discussion/Conclusions</h3> Image sharpening can create artifacts that could be misinterpreted. Although image sharpening significantly affects spatial resolution, no significant effect was seen on dynamic range or contrast resolution.

Image Fusion Technique: Algorithm to Application

This paper highlights the image fusion technique with two images ( one before flooding and another after flooding), thereby classifying the major land use categories in the flooding area. In July 2003, strong flooding occurred in Huai River, Anhui Province - China which caused serious harm to people's life and property. In order to reduce the loss, the detection of the flood inundated area and their land use categories need to be identified. Using multi-temporal images, during image fusion; the lacking information in one image is completed by another image. It substitutes missing information in one image with signals from another sensor image. The IHS sharpening technique is one of the most commonly used techniques for image sharpening. Different transformations have been developed to transfer a colour image from the RGB space to the IHS space. SAR image and TM multispectral image having different resolutions are fused giving higher resolution. The fused image is classified using unsupervised image classification technique with desired no of classes. The resulting classified image further divided into two categories only, namely disaster and non disaster area. Still, main task relies on how to categorize the disaster area into different land use classes and about their accuracies. The original image before flooding is classified using maximum likelihood classifier with desired number of classes. The maximum likelihood classifier performs its best due to the assumption of normal probability distribution of pixels, calculating probability density function for each pixel for each class and assigning them to the class where its occurrence is highest. So, when overall classification accuracy and KHAT statistic is good enough, final classification is made. The previously classified image with two categories and newly supervised classification are overlaid with proper transparency setting to get different land use categories.

Hyper-Sharpening Based on Spectral Modulation

Hyperspectral (HS) image sharpening (namely hyper sharpening) with an auxiliary sensor, such as multispectral (MS) or panchromatic sensor, has attracted a great deal of attention for the past decade. A number of hyper-sharpening techniques, aiming at enhancing the spatial resolution of HS images, have been developed and demonstrated their effectiveness especially on synthetic or simulated data. Nevertheless, since the differences between different remote sensing systems or imaging conditions are complicate, it results in a serious spectral distortion when applying on real remote sensing data acquired by different sensors under different acquisition times or conditions. Unfortunately, very few works have considered this issue. In this paper, a new hyper-sharpening framework based on spectral modulation is proposed to better preserve spectral information when fusing with MS data acquired by a different sensor. The goal of this paper is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Two approaches, originating in MS pan-sharpening field, are introduced as examples under this framework, namely high-pass details injection model and band-dependent spatial-detail model. Experiments on three HS and MS datasets acquired by different platforms demonstrate that the proposed framework is beneficial to the spectral fidelity of the fused image compared with some state-of-the-art hyper-sharpening techniques. Meanwhile, it is also easy to implement and has a certain advantage in enhancing the spatial details.

HAUSDORFF DERIVATIVE LAPLACIAN OPERATOR FOR IMAGE SHARPENING

Image sharpening based on the partial differential equations plays an important role in the fields of image processing. It is an effective technique to clear and sharpen image features, and provides a higher resolution for the subsequent processing. This paper makes the first attempt to employ the Hausdorff derivative Laplacian operator to sharpen the images. In terms of the visual quality of details, contours and edges, the original images and noisy images were sharpened by using an appropriate Hausdorff derivative order. Numerical results indicate that the Hausdorff derivative Laplacian operator outperforms the high-pass filtering, the Roberts operator and the traditional integer-order Laplacian operator. In comparison with the existing methods for the image sharpening, the proposed new methodology could be considered as a competitive alternative.

RESEARCH AND DEVELOPMENT OF METHODS FOR ENDOSCOPIC (MEDICAL) IMAGES ENHANCEMENT

Introduction. The modern technologies of virtual chromoendoscopy provide significant increase of diagnostic value of images considered by a doctor. The analysis of existing technologies shows that the existing solutions have significant disadvantages. Some of them require a complex preliminary calibration of the equipment for operation. Others use global transformations, making impossible consideration of local tissues characteristics and so on. In general, nowadays the technology of virtual chromoendoscopy, which suits the majority of potential users – doctors, does not exists, and, therefore, there it is a field for research.Objective. Development of the method for virtual chromoendoscopy, with regard to disadvantages identified within the frames of carried out analysis of similar methods.Methods and materials. For implementation of the research were used open endoscopic image data-bases, by the instrumentality of which, as a result of modeling and experiment, were evaluated quality characteris-tics of the proposed method.Results. The new method of virtual chromoendoscopy. The main feature of the method is usage of nonlinear local transformation functions in transformation of RGB channels, as well as absence of calibration procedure for obtaining the effect of virtual chromoendoscopy. The proposed method is completely based on the technology of digital image processing and includes image brightness correction, which provides the possibility to obtain the necessary visual information both from very dark and overexposed fragments; image sharpening, contrasting small details and vessels.Conclusion. The expert assessment of the obtained results shows that the visual effect of the proposed method corresponds, or in some cases, exceeds the visual effect of proprietary technologies of virtual endoscopy I-Scan and FICE.

A robust blind watermarking framework based on Dn structure

In this paper, a new robust watermarking framework is designed using the discrete cosine transform (DCT), binary decimal sequence (d-sequence) and Dn-structure. The core idea is to generate a binary d-sequence based on random number generator (RNG) and some secret keys. This binary sequence is then utilized to generate reference sets based on Dn-structure. For embedding purpose, the host image is transformed using DCT and selected coefficients are employed to construct a macro block. Each binary watermark bit is securely embedded into macro block by utilizing the reference set. The reverse process is finally formulated to extract the watermark at the receiver end. The proposed technique is experimentally analyzed using various quality metrics and attacks. The comprehensive experimental results illustrate that the proposed watermarking scheme is robust to both geometric distortions (resizing, cropping, wrapping and random row deletion) and general signal processing attacks (various noise addition, blurring, high pass filtering, image sharpening, contrast adjustment, Histogram equalization and gamma correction and JPEG compression) and outperforms state-of-the-art watermarking methods.

Spatial domain image enhancement techniques for acute myeloid leukemia (M1,M4,M5,M7)

&lt;p class="Abstract"&gt;&lt;span&gt;In this paper, several techniques of image enhancement spatial domain is elucidated and analyzed for the purpose of enhancing Acute Myeloid Leukemia (AML) subtype of M1, M4, M5 and M7. The techniques involved contrast stretching of greyscale images, image subtraction and image sharpening. The three methods compared with one another to achieve the highest PSNR value for the suitability technique of AML subtypes (M1, M4, M5 and M7). Firstly, subtypes images converted into grayscale. Then, each four images tested with contrast stretching techniques followed by image subtraction and image sharpening. The performances were evaluated based on Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Due to its higher value obtained, image sharpening is a good enhancement techniques for Acute Myeloid Leukemia with 68.2083 dB and the lowest MSE achieved of 0.0103.&lt;/span&gt;&lt;/p&gt;

Perancangan Sistem Deteksi Vena Pada Punggung Tangan Berbasis Inframerah Untuk Sistem Biometrik

AbstractVein detection is one of the imaging techniques required for biometric authentication purposes. The vein is chosen as an object because of its strength in terms of universality and uniqueness especially in adults because the venous pattern will only change slightly with age and each person has a different pattern, besides from the object side, the veins have no influence from temperature and humidity, and physical contactless with the other user. The purpose of this research is to create design that can visualize the veins as well and determine the optimum parameters to produce a better quality of vein image than the original image. The data for this research were obtained from 10 naracoba people who were varied of Boddy Mass Index and skin pigment. From the research, dorsal hand vein visualization tool for biometric system has been successfully designed using 726 nm infrared LED and image processing by convert of image to grayscale, image sharpening, histogram equalisation until segmentation by local thresholding method and the optimum parameters obtained to obtain a good image based on contrast analysis in this study is with a distance of 6 cm exposure.Keyword : Vein Detection, Biometrik, Near Infrared, Segmentation

Sharpening Method for Dynamic Images of Remote Network Video

Sharpening for dynamic images of remote network video is helpful to improve the dynamic images quality of remote network video and facilitate the subsequent use. Currently, most of remote network video dynamic images are completed based on the DSP chip, the cost of processing is high. In this paper, we propose a method to sharpen remote network video dynamic images based on the physical model. Firstly, image enhancement is carried out. Then, the dark channel priority method and the transmittance estimation method are analyzed to complete the sharpening. Experiments show that the proposed method can effectively improve the efficiency of image sharpening, and the sharpness of image is high and the practicability is strong.

Improving the Sharpness of Digital Images Using a Modified Laplacian Sharpening Technique

Many imaging systems produce images with deficient sharpness due to different real limitations. Hence, various image sharpening techniques have been used to improve the acutance of digital images. One of such is the well-known Laplacian sharpening technique. When implementing the basic Laplacian technique for image sharpening, two main drawbacks were detected. First, the amount of introduced sharpness cannot be increased or decreased. Second, in many situations, the resulted image suffers from a noticeable increase in brightness around the sharpened edges. In this article, an improved version of the basic Laplacian technique is proposed, wherein it contains two key modifications of weighting the Laplace operator to control the introduced sharpness and tweaking the second order derivatives to provide adequate brightness for recovered edges. To perform reliable experiments, only real-degraded images were used, and their accuracies were measured using a specialized no-reference image quality assessment metric. From the obtained experimental results, it is evident that the proposed technique outperformed the comparable techniques in terms of recorded accuracy and visual appearance.

Efficient network disruption under imperfect information: The sharpening effect of network reconstruction with no prior knowledge

The problem of network disruption has attracted widespread interests, for it currently appears in a myriad of contexts. However, existing methods require assuming that complete information of networks is known and spurious noise is not included, which is not always available in realistic situations. To overcome these limitations, this paper focuses on the problem of network disintegration with imperfect link information. We present a novel network disruption framework utilizing network reconstruction that is independent of prior knowledge and contains spurious links. Experiments in both synthetic and real networks reveal that, our approach which uses network reconstruction in advance, can obviously improve the network disruption performance. Moreover, we are surprised to find that, the disruption effect of our method is even superior to that achieved by complete information when the noise is relatively small. We name this phenomenon as the “sharpening effect” of network reconstruction, for its feature on reshaping network is similar to the image sharpening. Our results can be very useful for network disruption under incomplete information.

Combining SUN-based visual attention model and saliency contour detection algorithm for apple image segmentation

Accurate segmentation of apple fruit under natural illumination conditions provides benefits for growers to plan relevant applications of nutrients and pesticides. It also plays an important role for monitoring the growth status of the fruit. However, the segmentation of apples throughout various growth stages had only achieved a limited success so far due to the color changes of apple fruit as it matures as well as occlusion and the non-uniform background of apple images acquired in an orchard environment. To achieve the segmentation of apples with different colors and with various illumination conditions for the whole growth stage, a segmentation method independent of color was investigated. Features, including saliency and contour of the image, were combined in this algorithm to remove background and extract apples. Saliency using natural statistics (SUN) visual attention model was used for background removal and it was combined with threshold segmentation algorithm to extract salient binary region of apple images. The centroids of the obtained salient binary region were then extracted as initial seed points. Image sharpening, globalized probability of boundary-oriented watershed transform-ultrametric contour map (gPb-OWT-UCM) and Otsu algorithms were applied to detect saliency contours of images. With the built seed points and extracted saliency contours, a region growing algorithm was performed to accurately segment apples by retaining as many fruit pixels and removing as many background pixels as possible. A total of 556 apple images captured in natural conditions were used to evaluate the effectiveness of the proposed method. An average segmentation error (SE), false positive rate (FPR), false negative rate (FNR) and overlap Index (OI) of 8.4, 0.8, 7.5 and 90.5% respectively, were achieved and the performance of the proposed method outperformed other six methods in comparison. The method developed in this study can provide a more effective way to segment apples with green, red, and partially red colors without changing any features and parameters and therefore it is also applicable for monitoring the growth status of apples.

Dynamic turbulence mitigation for long-range imaging in the presence of large moving objects

Long-range imaging with visible or infrared observation systems is typically hampered by atmospheric turbulence. Software-based turbulence mitigation methods aim to stabilize and sharpen such recorded image sequences based on the image data only. Although successful restoration has been achieved on static scenes in the past, a significant challenge remains in accounting for moving objects such that they remain visible as moving objects in the output. Here, we investigate a new approach for turbulence mitigation on background as well as large moving objects under moderate turbulence conditions. In our method, we apply and compare different optical flow algorithms to locally estimate both the apparent and true object motion in image sequences and subsequently apply dynamic super-resolution, image sharpening, and newly developed local stabilization methods to the aligned images. We assess the use of these stabilization methods as well as a new method for occlusion compensation for these conditions. The proposed methods are qualitatively evaluated on several visible light recordings of real-world scenes. We demonstrate that our methods achieve a similar image quality on background elements as our prior methods for static scenes, but at the same time obtain a substantial improvement in image quality and reduction in image artifacts on moving objects. In addition, we show that our stabilization and occlusion compensation methods can be robustly used for turbulence mitigation in imagery featuring complex backgrounds and occlusion effects, without compromising the performance in less challenging conditions.

Implementation System of Human Eye Tracking Algorithm Based on FPGA

With the high-speed development of transportation industry, highway traffic safety has become a considerable problem. Meanwhile, with the development of embedded system and hardware chip, in recent years, human eye detection eye tracking and positioning technology have been more and more widely used in man-machine interaction, security access control and visual detection.In this paper, the high parallelism of FPGA was utilized to realize an elliptical approximate real-time human eye tracking system, which was achieved by the series register structure and random sample consensus (RANSAC), thus improving the speed of image processing without using external memory. Because eye images acquired by the camera often generate a lot of noises due to uneven light and dark background, the preprocessing technologies such as color conversion, image filtering, histogram modification and image sharpening were adopted. In terms of feature extraction of images, the eye tracking algorithm in this paper adopted seven-section rectangular eye tracking characteristic method, which increased a section between the mouth and the nose on the basis of the traditional six-section method, so its recognition accuracy is much higher. It is convenient for the realization of hardware parallel system in FPGA. Finally, aiming at the accuracy and real-time performance of the design system, a more comprehensive simulation test was carried out.The human eye tracking system was verified on DE2-115 multimedia development platform, and the performance of VGA (resolution: 640×480) images of 8-bit grayscale was tested. The results showed that the detection speed of this system was about 47 frames per second under the condition that the detection rate of human face (front face, no inclination) was 93%, which reached the real-time detection level. Additionally, the accuracy of eye tracking based on FPGA system was more than 95%, and it has achieved ideal results in real-time performance and robustness.

Remote Sensing Image Sharpening by Integrating Multispectral Image Super-Resolution and Convolutional Sparse Representation Fusion

In remote sensing, it is quite necessary to fuse spectral information of low-resolution multispectral (LRMS) images and spatial information of panchromatic (PAN) images for obtaining high-resolution multispectral (HRMS) images. In this paper, an effective fusion method integrating multispectral (MS) image super-resolution and convolutional sparse representation (CSR) fusion is proposed to make full use of the spatial information of remote sensing images. First, for enhancing the spatial information of LRMS images with suitable sizes, a fast iterative image super-resolution algorithm based on the learned iterative shrinkage and thresholding algorithm (LISTA) is exploited in the first stage. It employs a feed-forward neural network to simplify the solution of sparse coefficients in the process of super-resolution. In the fusion stage, we propose a CSR-based image fusion framework, in which each MS super-resolution image and PAN image is decomposed into a basic layer and a detail layer, then we fuse the basic layers and the detail layers of the images, respectively. This hierarchical fusion strategy guarantees great performance in detail preservation. The experimental results on QuickBird, WorldView-2, and Landsat ETM+ datasets demonstrate that the proposed method outperforms other methods in terms of both objective evaluation and visual effect.

Cases Study of Inputs Split Based Calibration Method for RRAM Crossbar

Many mainstream applications require multiply-accumulate calculations, such as image processing and neuromorphic computing. Multiply-accumulate calculations using memristor crossbar arrays is a remarkable method for extremely high implementation efficiency, whereas the memristor array fabrication technology is still not mature and it is difficult to fabricate large-scale arrays with high-yield, which will seriously affect the performance of the application running on the RRAM crossbar. This paper proposes an inputs split based calibration method that improves the application accuracy in tolerating variations and stuck-at-fault of memristor devices. To demonstrate the performance of the calibration algorithm, the case of image sharpening and three neural networks architectures are applied for simulation experiments. And experimental results show that the calculation accuracy can be improved by up to 26.83% at 90% yield of crossbar arrays, and the success rate of the algorithm can be as high as 99.3% when there are several arrays cascaded. It is of great significance to the application of arrays in multiply-accumulate computation.

Low-Power Approximate Unsigned Multipliers With Configurable Error Recovery

Approximate circuits have been considered for applications that can tolerate some loss of accuracy with improved performance and/or energy efficiency. Multipliers are key arithmetic circuits in many of these applications including digital signal processing (DSP). In this paper, a novel approximate multiplier with a low power consumption and a short critical path is proposed for high-performance DSP applications. This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbors for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery circuit. The approximate multipliers using these two error reduction strategies are referred to as AM1 and AM2, respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared with a Wallace multiplier optimized for speed, an $8\times 8$ AM1 using four most significant bits for error reduction shows a 60% reduction in delay (when optimized for delay) and a 42% reduction in power dissipation (when optimized for area). In a $16\times 16$ design, half of the least significant partial products are truncated for AM1 and AM2, which are thus denoted as TAM1 and TAM2, respectively. Compared with the Wallace multiplier, TAM1 and TAM2 save from 50% to 66% in power, when optimized for area. Compared with existing approximate multipliers, AM1, AM2, TAM1, and TAM2 show significant advantages in accuracy with a low power-delay product. AM2 has a better accuracy compared with AM1 but with a longer delay and higher power consumption. Image processing applications, including image sharpening and smoothing, are considered to show the quality of the approximate multipliers in error-tolerant applications. By utilizing an appropriate error recovery scheme, the proposed approximate multipliers achieve similar processing accuracy as exact multipliers, but with significant improvements in power.

Image Multiplier Based on Low Power Approximate Unsigned Multiplier

Approximate circuits have been considered for applications that can tolerate some loss of accuracy with improved performance and/or energy efficiency. Multipliers are key arithmetic circuits in many of these applications including digital signal processing (DSP). This multiplier leverages a newly designed approximate adder that limits its carry propagation to the nearest neighbours for fast partial product accumulation. Different levels of accuracy can be achieved by using either OR gates or the proposed approximate adder in a configurable error recovery circuit. The approximate multipliers using these two error reduction strategies are referred to as AM1 and AM2, respectively. Both AM1 and AM2 have a low mean error distance, i.e., most of the errors are not significant in magnitude. Compared with a Wallace multiplier optimized for speed, an 8×8 AM1 using four most significant bits for error reduction shows a 60% reduction in delay (when optimized for delay) and a 42% reduction in power dissipation (when optimized for area). In a 16×16 design, half of the least significant partial products are truncated for AM1 and AM2, which are thus denoted as TAM1 and TAM2, respectively. Compared with the Wallace multiplier, TAM1 and TAM2 save from 50% to 66% in power, when optimized for area. Compared with existing approximate multipliers, AM1, AM2, TAM1, and TAM2 show significant advantages in accuracy with a low power-delay product. AM2 has a better accuracy compared with AM1 but with a longer delay and higher power consumption. Image processing applications, including image sharpening and smoothing, are considered to show the quality of the approximate multipliers in error-tolerant applications. By utilizing an appropriate error recovery scheme, the proposed approximate multipliers achieve similar processing accuracy as exact multipliers, but with significant improvements in power.

An Enhanced Approach for Automation the Diagnosis of Iron Deficiency Anemia Based on Quantitative Analysis of Red Blood Cells in Intestine Villi Tissue

Recent years have witnessed a huge revolution in developing automated diagnosis for different diseases such as cancer using medical image processing. Many researchers have been conducted in this field. Analyzing medical microscopic images provide pathology medical track with large information about the status of the patients and the progress of the diseases and help in detecting any pathological changes in tissues. Automation of the diagnosis of these images will lead to a better, faster and enhanced diagnosis for different hematological and histological images. This paper proposes an automated approach for analyzing blood smear microscopic images to help in diagnosing anemia using quantitative analysis of red blood cells in intestine villi tissue. The diagnoses depends on counting the number of blue and red stained blood cells that contain iron in each villi separately, then, it calculates the percentage of blue cells and red cells in the experimented image. The experimental results have shown that using digital image processing techniques through processing the image into different stages as including noise removal, image sharpening, enhancing contrast, find region of interest, isolating color, removing edges, and counting cells leads to a successful outcome and the diagnose of anemia.

Research on optimization of image fast feature point matching algorithm

The author studied the feature point extraction and matching based on BRISK and ORB algorithms, experimented with the advantages of both algorithms, and ascertained optimal pyramid layer and inter-layer scale parameters used in features extraction and matching for the same scale image and different scale images with BRISK and ORB algorithm, and analyzed the effectiveness of different parameters combinations on the accuracies of feature extraction and matching and proposed method to determine parameters based on the results. In addition, comparing with the traditional algorithm, using the optimal algorithm with the parameters combining Gaussian denoising, graying, and image sharpening, the ratio of feature points for detection improved 3%; the number of effective matching points increased by nearly 2%. Meanwhile, an algorithm experiment on UAV image mosaic was carried out. The transition of mosaic image color was more natural, and there was no clear mosaic joint with the stitching effect, which indicated that the optimized parameters and the extracted feature point pairs can be used for matrix operations and the algorithm is suitable for UAV image mosaic processing.