Shift-and-add image processing incorporated with the unsharp masking method.

SUMMARYTo restrain pseudo‐Gibbs phenomenon, low contrast and blurred phenomenon in the process of image enhancement, a new method based on the nonsubsampled contourlet transform and the unsharp masking is proposed in this paper. The proposed method utilizes the shift‐invariance of nonsubsampled contourlet transform to restrain the pseudo‐Gibbs phenomenon, and then enhance details of the image by unsharp masking. We achieved an increase in image definition by 54.5%, the mean increased by 15.6%, whereas the standard deviation increased by 54.5% compared with the unsharp masking method. To the noisy image, we achieved an increase in image definition by 35.4%, the mean increased by 2.2%, whereas the standard deviation increased by 34.9% compared with the unsharp masking method. Copyright © 2013 John Wiley & Sons, Ltd.

Technical limitations in image capturing usually impose defective, such as contrast degradation. There are different approaches to improve the contrast of an image. Among the exiting approaches, un-sharp masking is a popular method due to its simplicity in implementation and computation. There is an important parameter in un-sharp masking, named gain factor, which affects the quality of the enhanced image. In this paper, a new adaptive un-sharp masking method is proposed. In the proposed method gradient variation of the image is used to estimate the gain factor for un-sharp masking. Gradient variation of an image can provide information about the image contrast. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed un-sharp masking methods. The experimental results show the superiority of the proposed method compared to the existing methods in image enhancing using un-sharp masking.

Digital camera and mobile document image acquisition are new trends arising in the world of Optical Character Recognition and text detection. In some cases, such process integrates many distortions and produces poorly scanned text or text-photo images and natural images, leading to an unreliable OCR digitization. In this paper, we present a novel nonparametric and unsupervised method to compensate for undesirable document image distortions aiming to optimally improve OCR accuracy. Our approach relies on a very efficient stack of document image enhancing techniques to recover deformation of the entire document image. First, we propose a local brightness and contrast adjustment method to effectively handle lighting variations and the irregular distribution of image illumination. Second, we use an optimized greyscale conversion algorithm to transform our document image to greyscale level. Third, we sharpen the useful information in the resulting greyscale image using Un-sharp Masking method. Finally, an optimal global binarization approach is used to prepare the final document image to OCR recognition. The proposed approach can significantly improve text detection rate and optical character recognition accuracy. To demonstrate the efficiency of our approach, an exhaustive experimentation on a standard dataset is presented

Many advanced ground-based solar telescopes improve the spatial resolution of observation images using an adaptive optics (AO) system. As any AO correction remains only partial, it is necessary to use post-processing image reconstruction techniques such as speckle masking or shift-and-add (SAA) to reconstruct a high-spatial-resolution image from atmospherically degraded solar images. In the New Vacuum Solar Telescope (NVST), the spatial resolution in solar images is improved by frame selection and SAA. In order to overcome the burden of massive speckle data processing, we investigate the possibility of using the speckle reconstruction program in a real-time application at the telescope site. The code has been written in the C programming language and optimized for parallel processing in a multi-processor environment. We analyze the scalability of the code to identify possible bottlenecks, and we conclude that the presented code is capable of being run in real-time reconstruction applications at NVST and future large aperture solar telescopes if care is taken that the multi-processor environment has low latencies between the computation nodes.

In medical ultrasound B-mode imaging, by increasing the image contrast, spatial and temporal resolution, it will help to improve the diagnostic and decision making process. Previously the unsharp masking (UM) techniques have been successfully implemented for digital image processing as edge enhancement techniques. However, the outcomes of the method are limited and influenced by the image formats conversion and lower dynamic range. Moreover the conventional UM technique sensitive to noise due to the presence of linear high pass filter which cannot discriminate signal from noise. Furthermore, the technique only enhances the image in darker compared to lighter regions. To overcome those issues, a new improved UM method compatible with ultrasound B-mode imaging was applied to compound plane wave imaging (CPWI). The received radio frequency (RF) signal was beamformed with delay and sum (DAS) and filtered delay and sum (FDMAS). Results show that the proposed techniques are not only able to improve the image contrast but also the spatial resolution. The UM technique manages to improve the B-mode image lateral resolution (LR) by 18.36% and 10.25% as well as reduced the peak side lobes (PSL) by 16.67 dB and 21.24 dB on DAS and FDMAS respectively when compounded with CPWI, N=13. For the same number of compounding and beamforming order, the image contrast ratio (CR) has been improved by 10.35 dB and 7.39 dB accordingly.

It has been observed that enhancing a highly-blurred image region could often lead to unpleasant noise amplification. Motivated by this, an adaptive unsharp masking (UM) method is proposed in this paper, which incorporates the estimated local blurriness information into the enhancement process to adaptively determine the scaling factor for each pixel on the detail layer. To achieve this goal, a pixel-wise local blurriness estimation method is developed for generating a pixel-wise blurriness map, followed by individually converting each blurriness measurement on the map to a scaling factor via a mapping process. The proposed method not only avoids noise amplification in blurred regions but also addresses ‘high-level’ considerations, such as photographer's original intention on making background more blurred for creating special aesthetic effect. Extensive simulations conducted on various test images have demonstrated that our approach is able to deliver much superior perceptual quality of enhanced images compared to other state-of-the-art UM methods.

The motive of palmprint image enhancement is to highlight the fine details of palm lines. In this paper, a fuzzy unsharp masking algorithm is presented to enhance the contrast of a palmprint image. This method introduces the fuzzy set theory into the unsharp masking scheme. Unsharp masking (UM) approaches are simple and effective for contrast enhancement of high-frequency components. Most of UM algorithms can effectively improve the contrast of principal lines, but insensitive to wrinkles. In order to further enhance the contrast of wrinkles, a half open membership function is used to transform the output of the filters into fuzzy domain. The palmprint image is enhanced in fuzzy field. The proposed method is compared with other techniques in the paper and the experimental result demonstrates that the proposed approach can enhance the fine details of palmprint image effectively.

The shift-and-add (SAA) method is used to estimate the magnitude difference of binary star components. A simple procedure based on the SAA method enables an accurate estimation of the magnitude difference. Some computer simulations confirm this. The use of this procedure in stellar speckle interferograms is also demonstrated.

To determine the preferences of radiologists among eight different image processing algorithms applied to digital mammograms obtained for screening and diagnostic imaging tasks. Twenty-eight images representing histologically proved masses or calcifications were obtained by using three clinically available digital mammographic units. Images were processed and printed on film by using manual intensity windowing, histogram-based intensity windowing, mixture model intensity windowing, peripheral equalization, multiscale image contrast amplification (MUSICA), contrast-limited adaptive histogram equalization, Trex processing, and unsharp masking. Twelve radiologists compared the processed digital images with screen-film mammograms obtained in the same patient for breast cancer screening and breast lesion diagnosis. For the screening task, screen-film mammograms were preferred to all digital presentations, but the acceptability of images processed with Trex and MUSICA algorithms were not significantly different. All printed digital images were preferred to screen-film radiographs in the diagnosis of masses; mammograms processed with unsharp masking were significantly preferred. For the diagnosis of calcifications, no processed digital mammogram was preferred to screen-film mammograms. When digital mammograms were preferred to screen-film mammograms, radiologists selected different digital processing algorithms for each of three mammographic reading tasks and for different lesion types. Soft-copy display will eventually allow radiologists to select among these options more easily.

Observer performance experiments using ROC analysis indicated that the detectability of subtle microcalcifications in digitized mammograms was improved by unsharp-mask filtering. However, the processed mammograms still provided lower accuracy than the original mammogram. Similar studies with mild interstitial infiltrates and subtle pneumothoraces in chest radiographs showed that the use of a pixel size substantially larger than 0.1 mm may result in some loss of diagnostic accuracy, and that a 2048×2048 matrix may be a satisfactory compromise for digital chest radiography. The unsharp masking may improve diagnostic accuracy for pneumothorax, while radiologists' performance in identifying interstitial infiltrates was degraded by the image processing. The contrast-detail (C-D) diagram was measured by using simulated square patterns superimposed on radiographic noise. Experimental variations in conventional C-D curves due to observer differences, within-reader variability, and image noise outcomes were quite large, and therefore it was necessary to increase the total number of observations in order to obtain reliable results. The relationship between physical and visual image quality for the task of detecting plastic beads was studied on thirty-seven different radiographic imaging conditions using a 2AFC visual detection experiment. Results indicated that human observer detection performance most closely resembled that of a sub-optimal statistical decision process. The effects of imaging and display conditions on the detectability of low-contrast objects in DSA images were investigated by using 18AFC experiments. We found that the SNR obtained from the perceived statistical decision theory model, which includes the observer's internal noise, can accurately predict the detectability of low-contrast objects in DSA images. The effect of pixel size on the SNR and threshold detection of low-contrast radiologic pattern was investigated theoretically for digital radiographic systems. No substantial difference between 0.1 and 0.2 mm pixel sizes was found in threshold contrasts for square objects. No substantial difference between 0.1 and 0.2 mm pixel sizes was found in threshold contrasts for square objects ranging from 0.1 to 20 mm, and thus we conclude that 0.2 mm pixel would be sufficiently small for visualization of most image detail in many radiologic examinations. The effects of image processing with unsharp masking, Metz filters, matched filters and optimal statistical filter on the detection of simulated square objects superimposed on radiographic mottle were studied theoretically and experimentally. The usefulness and limitations of these filters were discussed.

Nowadays, digital images can be easily obtained, recorded and processed in accordance with the widespread use of digital cameras and personal computers. There is an image enhancement as one of most general image processing techniques. In the image enhancement, unsharp masking (UM) has been used widely because its algorithm is very simple and can produce good results. However, over-enhancement occurs in edge parts of the image. Additionally UM also is easy to generate artifacts such as jaggy and halo. To cope with these problems, the authors proposed the selective UM method based on the edge-preserved smoothing filter. However, the image quality obtained by our method depends on values of the enhancement parameters, and their optimal values vary according to user's liking. In this paper, we propose an image enhancement system considering the user's liking by using an interactive genetic algorithm (IGA). In the proposed system, the IGA optimizes the parameters of the image enhancement. Further, a new genetic operation is introduced in order to achieve the effective search in the IGA of the proposed system. Through the experiments using some images, the effectiveness of the proposed system is verified.

The scanning beam digital x-ray system (SBDX) is an inverse geometry fluoroscopic system with high dose efficiency and the ability to perform continuous real-time tomosynthesis in multiple planes. This system could be used for image guidance during lung nodule biopsy. However, the reconstructed images suffer from strong out-of-plane artifact due to the small tomographic angle of the system. The authors propose an out-of-plane artifact subtraction tomosynthesis (OPAST) algorithm that utilizes a prior CT volume to augment the run-time image processing. A blur-and-add (BAA) analytical model, derived from the project-to-backproject physical model, permits the generation of tomosynthesis images that are a good approximation to the shift-and-add (SAA) reconstructed image. A computationally practical algorithm is proposed to simulate images and out-of-plane artifacts from patient-specific prior CT volumes using the BAA model. A 3D image registration algorithm to align the simulated and reconstructed images is described. The accuracy of the BAA analytical model and the OPAST algorithm was evaluated using three lung cancer patients' CT data. The OPAST and image registration algorithms were also tested with added nonrigid respiratory motions. Image similarity measurements, including the correlation coefficient, mean squared error, and structural similarity index, indicated that the BAA model is very accurate in simulating the SAA images from the prior CT for the SBDX system. The shift-variant effect of the BAA model can be ignored when the shifts between SBDX images and CT volumes are within ±10 mm in the x and y directions. The nodule visibility and depth resolution are improved by subtracting simulated artifacts from the reconstructions. The image registration and OPAST are robust in the presence of added respiratory motions. The dominant artifacts in the subtraction images are caused by the mismatches between the real object and the prior CT volume. Their proposed prior CT-augmented OPAST reconstruction algorithm improves lung nodule visibility and depth resolution for the SBDX system.

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Contrast enhancement has played an important role in image processing. A variety of enhancement algorithms now existing mainly focus on the overall contrast of digital images while sometimes we may wish to know much about details such as the lung capillaries and so on. The unsharp masking image enhancement techniques allow enhancing very small structures but are not accessible for larger size objects. Gray-level Grouping (GLG) method is effective to optimize the overall enhancement. In addition, multi-scale decomposition of the images has been proven to be capable and efficient method to cope with digital images. So we combined all of the three methods to enhance the digital radiographs. The experiment shows that contrast and details have been improved greatly.

Dark spots, which are widely present in different species of eggs, not only significantly affect the appearance and reduce the commercial value of eggs, but also increase the safety hazards of edible eggs in view of that Salmonella can easily penetrate the eggshell at the location of dark spots. During the first 5 days after egg production, it is difficult to identify and evaluate dark spots on the eggshell surface under natural lighting conditions. Therefore, it is a great challenge to automatically classify commercial eggs according to the amount of dark spots at the initial stage. In this paper, a method based on machine vision was proposed for identifying and evaluating eggshell dark spots. First, the K-means clustering algorithm was used to segment the individual egg image on the production line in order to obtain the complete eggshell surface area; then, the unsharp masking method was used to enhance the dark-spot features so as to realize the recognition of dark spots; and finally, quantitative evaluation was conducted according to the amount of dark spots on the eggshell surface and the ratio of the dark-spot projected area. Our experimental results show that the proposed method is able to quickly and accurately calculate the distribution of dark spots and the ratio of the dark-spot projected area. Specifically, the processing speed of dark-spot image is 1 frame/0.5s, which is 960 times faster than the speed of manual marking (1 frame/480s), and the detection capacity of the experimental device is 3600 eggs/h. It provides an automated method for quantitatively examining dark spots on eggshells, a scientific tool for conducting further research on the formation mechanism of dark spots, as well as a technical means for the high-throughput online examination of egg quality.