Decline In Image Quality Research Articles

Landsat 9 Thermal Infrared Sensor-2 (TIRS-2) Pre- and Post-Launch Spatial Response Performance

The launch of Landsat 9 (L9) on 27 September 2021 marks the ongoing commitment of the Landsat mission to delivering users with calibrated Earth observations for fifty years. The two imaging sensors on L9 are the Thermal Infrared Sensor-2 (TIRS-2) and the Operational Land Imager-2 (OLI-2). Shortly after launch, the image data from OLI-2 and TIRS-2 were evaluated for both radiometric and geometric quality. This paper provides a synopsis of the evaluation of the spatial response of the TIRS-2 instrument. The assessment focuses on determining the instrument’s ability to detect a perfect knife edge. The spatial response was evaluated both pre- and post-launch. Pre-launch testing was performed at NASA Goddard Space Flight Center (GSFC) under flight-like thermal vacuum (TVAC) conditions. On orbit, coastline targets were identified to evaluate the spatial response and compared against Landsat 8 (L8). The pre-launch results indicate that the spatial response of the TIRS-2 sensor is consistent with its predecessor on board L8, with no noticeable decline in image quality to compromise any TIRS science objectives. Similarly, the post-launch analysis shows no apparent degradation of the TIRS-2 focus during the launch and the initial operational timeframe.

Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.

An unsolvable pixel reduced authentication method for color images with grayscale invariance

This paper introduces an advanced grayscale-invariant authentication method for color images. Prior work directly utilizes the most significant bits (MSBs) of the red and blue channels for authentication code generation and embeds these codes into the least significant bits (LSBs). Concurrently, the green channel is adjusted to sustain consistent grayscale values. However, this direct utilization of MSBs often leads to a decline in image quality and results in a significant number of unsolvable pixels. To address these challenges, we present a new approach specifically designed to combat the deterioration of image quality and the prevalence of unsolvable pixels. In our method, we propose the MSB alteration (MSBA) technique, which modifies the MSBs of the red and blue channels to generate authentication codes with minimal distortion. This technique effectively reduces image distortion and the number of unsolvable pixels. Furthermore, we introduce the pixel substitution strategy (PSS) and the constraint-reduced strategy (CRS) to enhance the embedding efficiency. Experimental results demonstrate that our method outperforms prior work by converting more unsolvable pixels to solvable ones and achieving better detection results. Moreover, our method enhances image quality by approximately 1.5 dB, while maintaining the same embedding capacity.

SIRT methods for the iterative solution of sparse OPT data reconstruction

Optical projection tomography (OPT) is an advanced three-dimensional (3D) imaging technology, which uses the filtered backprojection (FBP) algorithm to recover the 3D volume with sufficient number of projections. As to in vivo imaging, it is urgent to reduce the number of projections because the acquisition time could be minimized. However, reconstructing from undersampled OPT data can lead to artifacts and the decline in image quality. The simultaneous iterative reconstruction technique (SIRT) is introduced to remove artifacts and improve the image quality. The image qualities reconstructed from FBP and SIRT separately are compared, and the structural similarity and peak signal-to-noise ratio are calculated. Through simulated phantoms and OPT data of in vivo zebrafish embryo, SIRT consistently outperforms FBP in terms of reduced artifacts and enhanced image contrast especially when the projection numbers are reduced. SIRT method can provide high-quality reconstruction with 50 or fewer projections, thereby significantly reducing the minimum acquisition time and light dose while maintaining reconstruction quality. Through optimization and GPU acceleration, the SIRT algorithm can converge faster so as to reduce the image processing time. To our knowledge, this is the first time one SIRT algorithm is used for reconstruction of sparse OPT data. The experimental results show that SIRT algorithm outperform FBP especially when the number of projections is reduced. In addition, SIRT performs better in the preservation of vascular signal, which is significant for the monitoring of angiogenesis.

A high-efficiency acquisition method of LED multispectral images using Gray code based square wave frequency division modulation

LED illumination-based multispectral imaging (LEDMSI) is a fast, convenient, and effective multispectral image acquisition technique. The square wave frequency division modulation (SFDM) and the integral sampling of the camera can build an efficient match pattern. With regards to image quality decline problems due to the ringing effect caused by the drive module while square wave high-low level conversion in the SFDM-LEDMS system. This paper proposes a multispectral image acquisition method combining Gray code and square wave frequency modulation. The micro control unit was used to generate a series of frequency division square waves in Gray code mode followed by LEDs of different wavelengths modulated by SFDM-square waves as a light source. Subsequently, the camera was used to simultaneously collect a sequence of multi-spectral images followed by single-wavelength images using differential accumulation demodulation. Finally, the no-reference image quality assessment (NR-IQA) was used to evaluate the quality of each single-wavelength image obtained by Binary code and Gray code. We deployed five wavelengths as examples to verify the effectiveness of the proposed method. The accumulated results indicated an improvement of the image quality in terms of information and gradient using Gray code. In conclusion, the present study can provide an effective image acquisition alternative for household medical equipment.

Image haze removal based on rolling deep learning and Retinex theory

Multispectral remote sensing images are a very important data source, but its acquisition process is often affected by haze weather and other factors, resulting in the decline of image quality, blurred details and poor visual effect, which seriously affect their application and interpretation. To reduce the impact of haze on multispectral remote sensing image and improve the image clarity and the use value, a new haze removal method based on rolling deep learning and Retinex theory (RDLRT) was proposed here. It uses the rolling deep learning theory to realize the preliminary removal of image haze, and then calculates the peak signal-to-noise ratio (PSNR) value of the processed image, which is used to judge whether the obtained image is a stage effect image (SEI). Then, on the one hand, the SEI image is processed for colour saturation and brightness; on the other hand, the Retinex theory is used to further filter the haze of the SEI image. Finally, the results of the two processing are fused, and it is used as the restored image after removing the haze. A series of validation experiments were carried out with true multispectral remote sensing images, outdoor colour images and different methods, and good experimental results were obtained. The purpose of removing image haze and improving image quality is achieved. Through the comparison of these experiments, it is concluded that the RDLRT algorithm proposed in this paper can not only effectively remove haze in images, but also maintain good details and colour of the image, which has some popularization values for image pre-processing and restoration.

Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

Unsupervised learning polarimetric underwater image recovery under nonuniform optical fields.

Turbid media will lead to a sharp decline in image quality. Polarization imaging is an effective method to obtain clear images in turbid media. In this paper, we propose an improved method that combines unsupervised learning and polarization imaging theory, which can be applied in a variety of nonuniform optical fields. We treat the background light as a spatially variable parameter, so we designed an end-to-end unsupervised generative network to inpaint the background light, which produces an adversarial loss with the discriminative network to improve the performance. And we use the angle of polarization to estimate the polarization parameters. The experimental results have demonstrated the effectiveness and generalization ability of our method. Compared with other works, our method shows a better real-time performance and has a lower cost in preparing the training dataset.

Automatic Shadow Compensation Based on Improved Wallis Model for High Resolution Remote Sensing Images

Objectives Shadows in high-resolution remote sensing images will cause objects information loss and image quality decline, which is not beneficial for relative applications. Current shadow compensation methods often take advantage of non-shadow information around the shadow area to increase the brightness, whereas there is an unsolved problem that the contrast cannot be enhanced well enough and self-adaptively. Wallis filter principle has been used in image dodging. However, when it is used in shadow compensation, contrast improvement is not as good as other methods, leading to poor compensation results. Therefore, an improved Wallis model compensation method is proposed in this paper to enhance the brightness and contrast better to restore the shaded information. Methods First, by adding compensation strength and stretch parameters, an improved Wallis model is designed. The strength parameter is positive to the brightness and contrast, and the stretch parameter is sensitive to the contrast. Therefore, the improved Wallis model is more efficient to adjust brightness and contrast. Moreover, an automatic parameters calculation strategy is further explored to customize a suitable compensation model for each shadow area. On one hand, the brightness average and deviation of the adjacent non-shadow region are calculated and used as the compensation target values. On the other hand, based on searching the same kinds of points around shadow boundaries, a series of non-shadow and shadow feature points are matched. Assumed the feature value of the non-shadow point is the approximate value of its responding shadow point, they can be used to calculate strength and stretch parameters automatically. Lastly, the brightness of each pixel in different shadow regions will be compensated by customized models to cover the shaded information self-adaptively. Results In this paper, three sets of comparative experiments are set up to analyze and compare the part compensation methods algorithm, original Wallis method in three images with ordinary object shadows and cloud shadows, respectively. The differences in brightness average and gradient average between the compensated value and the non-shadow target value are used to evaluate the compensation quality. The experimental results show that: (1) Original Wallis model is useful to enhance brightness to some extent, while it is insufficient to improve contrast. As a consequence, the visual compensated results are not acceptable as the other two methods. (2) Part compensation method can improve brightness and contrast effectively. However, it cannot adjust self-adaptively according to each shadow regions condition, leading to some over-compensated and some insufficient compensation results in the same image. (3) The proposed method results indicate the best compensation quality in different shadow regions because the improved Wallis model is more pointed to improve contrast and the automatically calculated parameters values are suitable to customize the compensation model for each shadow region. Conclusions Aiming at the contrast improvement of the current algorithm of automatic shadow compensation, an automatic compensation method based on an improved Wallis model is proposed in this paper. The experimental results show that the newly designed model is more effective to enhance brightness and contrast, which is useful for finding suitable parameters values. Combining with the strategy of automatic parameters calculation, its customized model can adaptively compensate each shadow. However, it should be pointed out that there are still some limitations about the shadow border compensation and the internal difference in one shadow area, which need to be further studied.

Data embedding scheme based on multi-matrix structure of turtle shell to avoid human eye perception

Data embedding is a technique for embedding secret information into the carrier data, such as text, audios, images, and videos. Meanwhile, it also can ensure that the changes in the carrier data are imperceptible by human eye to avoid attracting the attention of malicious attackers. In this paper, we propose a data embedding scheme based on the multi-matrix structure of turtle shell (MMS-TDH). First, weighting is introduced to divide the cover image into various regions, i.e., a smooth region, a texture region and an edge region. Second, considering that the human eye is more sensitive to changes in the pixels in the smooth region than in the other regions, the smaller turtle shell matrix was used to ensure fewer changes in the pixels of the smooth region, and the larger turtle shell matrix was used for the edge region to improve the embedding capacity. This process ensures the flexibility of our scheme in balancing embedding capacity and image quality, and it also avoids the sharp decline in image quality when the turtle shell matrix enlarges. Extensive experimental results validated the expected merits of the proposed scheme.

Image quality recovery in binary ghost imaging by adding random noise.

When the sampling data of ghost imaging are recorded with less bits, i.e., experiencing quantization, a decline in image quality is observed. The fewer bits that are used, the worse the image one gets. Dithering, which adds suitable random noise to the raw data before quantization, is proved to be capable of compensating image quality decline effectively, even for the extreme binary sampling case. A brief explanation and parameter optimization of dithering are given.

Sliding window prior data assisted compressed sensing for MRI tracking of lung tumors.

Hybrid magnetic resonance imaging and radiation therapy devices are capable of imaging in real-time to track intrafractional lung tumor motion during radiotherapy. Highly accelerated magnetic resonance (MR) imaging methods can potentially reduce system delay time and/or improves imaging spatial resolution, and provide flexibility in imaging parameters. Prior Data Assisted Compressed Sensing (PDACS) has previously been proposed as an acceleration method that combines the advantages of 2D compressed sensing and the KEYHOLE view-sharing technique. However, as PDACS relies on prior data acquired at the beginning of a dynamic imaging sequence, decline in image quality occurs for longer duration scans due to drifts in MR signal. Novel sliding window-based techniques for refreshing prior data are proposed as a solution to this problem. MR acceleration is performed by retrospective removal of data from the fully sampled sets. Six patients with lung tumors are scanned with a clinical 3 T MRI using a balanced steady-state free precession (bSSFP) sequence for 3min at approximately 4 frames per second, for a total of 650 dynamics. A series of distinct pseudo-random patterns of partial k-space acquisition is generated such that, when combined with other dynamics within a sliding window of 100 dynamics, covers the entire k-space. The prior data in the sliding window are continuously refreshed to reduce the impact of MR signal drifts. We intended to demonstrate two different ways to utilize the sliding window data: a simple averaging method and a navigator-based method. These two sliding window methods are quantitatively compared against the original PDACS method using three metrics: artifact power, centroid displacement error, and Dice's coefficient. The study is repeated with pseudo 0.5 T images by adding complex, normally distributed noise with a standard deviation that reduces image SNR, relative to original 3 T images, by a factor of 6. Without sliding window implemented, PDACS-reconstructed dynamic datasets showed progressive increases in image artifact power as the 3min scan progresses. With sliding windows implemented, this increase in artifact power is eliminated. Near the end of a 3min scan at 3 T SNR and 5× acceleration, implementation of an averaging (navigator) sliding window method improves our metrics by the following ways: artifact power decreases from 0.065 without sliding window to 0.030 (0.031), centroid error decreases from 2.64 to 1.41mm (1.28mm), and Dice coefficient agreement increases from 0.860 to 0.912 (0.915). At pseudo 0.5 T SNR, the improvements in metrics are as follows: artifact power decreases from 0.110 without sliding window to 0.0897 (0.0985), centroid error decreases from 2.92mm to 1.36mm (1.32mm), and Dice coefficient agreements increases from 0.851 to 0.894 (0.896). In this work we demonstrated the negative impact of slow changes in MR signal for longer duration PDACS dynamic scans, namely increases in image artifact power and reductions of tumor tracking accuracy. We have also demonstrated sliding window implementations (i.e., refreshing of prior data) of PDACS are effective solutions to this problem at both 3 T and simulated 0.5 T bSSFP images.

Very short‐term prediction model for photovoltaic power based on improving the total sky cloud image recognition

Due to the cloud cover, the power generation of photovoltaic power plant will reduce suddenly, that may lead to instability of the grid and bring some risks. Cloud observation is often used in the very shortterm prediction of photovoltaic power. However, in the haze weather, The quality of the total sky image will fall, the cloud recognition effect is reduced, further leads to decreased levels of photovoltaic power prediction.This study presents a photovoltaic power prediction algorithm,which takes into account the image quality decline caused by haze. So the algorithm improves the precision of photovoltaic power generation. Firstly, the extraction method using discrete Fourier transform checks whether clouds exist or not, and computing in the position of the solar facula, and then fix the facula. Next, using piece wise linear transformation algorithm for cloud enhancement, and then generate intensity layered cloud stain chart. Finally, the relationship between cloud and radiation is used to realise the very short-term prediction of photovoltaic power. The experimental results show that the algorithm has a very good universality, and the cloud image can be identified by the fog and haze, therefore it improves the accuracy of the ultra-short term prediction of photovoltaic power.

Image quality metric based on regular structure features

The objective assessment of image quality has been extensively studied in recent years, and a large number of evaluation algorithms have been presented. The existing evaluation algorithms of image quality has already exhibited satisfactory performances in many common image distortion models, such as additive noise, grayscale adjustment, low pass filter, motion blurring, lossy compression, etc. However, most of them have no effect on the geometric distortion, which is widely existed in the digital images. In view of geometric distortion, a full-reference image quality assessment method is presented in this paper. The core idea of this method is that only the geometric distortion acting on regular structure features of image can have significant impacts on the perceptive quality of the image, and the physical strength and spatial irregularity of geometric distortion are the main factors causing the decline of image quality. According to experimental data, the proposed assessment method can be well applied to geometric distortion model, the evaluation results are better than most of the existing methods, and it has showed significant correlation with MOS.

SU-G-JeP1-15: Sliding Window Prior Data Assisted Compressed Sensing for MRI Lung Tumor Tracking

Purpose: Prior Data Assisted Compressed Sensing (PDACS) is a partial k-space acquisition and reconstruction method for mobile tumour (i.e. lung) tracking using on-line MRI in radiotherapy. PDACS partially relies on prior data acquired at the beginning of dynamic scans, and is therefore susceptible to artifacts in longer duration scan due to slow drifts in MR signal. A novel sliding window strategy is presented to mitigate this effect. Methods: MRI acceleration is simulated by retrospective removal of data from the fully sampled sets. Six lung cancer patients were scanned (clinical 3T MRI) using a balanced steady state free precession (bSSFP) sequence for 3 minutes at approximately 4 frames per second, for a total of 650 dynamics. PDACS acceleration is achieved by undersampling of k-space in a single pseudo-random pattern. Reconstruction iteratively minimizes the total variations while constraining the images to satisfy both the currently acquired data and the prior data in missing k-space. Our novel sliding window technique (SW-PDACS), uses a series of distinct pseudo-random under-sampling patterns of partial k-space – with the prior data drawn from a sliding window of the most recent data available. Under-sampled data, simulating 2 – 5x acceleration are reconstructed using PDACS and SW-PDACS. Three quantitative metrics: artifact power, centroid error and Dice's coefficient are computed for comparison. Results: Quantitively metric values from all 6 patients are averaged in 3 bins, each containing approximately one minute of dynamic data. For the first minute bin, PDACS and SW-PDACS give comparable results. Progressive decline in image quality metrics in bins 2 and 3 are observed for PDACS. No decline in image quality is observed for SW-PDACS. Conclusion: The novel approach presented (SW-PDACS) is a more robust for accelerating longer duration (>1 minute) dynamic MRI scans for tracking lung tumour motion using on-line MRI in radiotherapy. B.G. Fallone is a co-founder and CEO of MagnetTx Oncology Solutions (under discussions to license Alberta bi-planar linac MR for commercialization).

Effects of Breast Shielding during Heart Imaging on DNA Double-Strand-Break Levels: A Prospective Randomized Controlled Trial.

Purpose To examine the effect of breast shielding on blood lymphocyte deoxyribonucleic acid (DNA) double-strand-break levels resulting from in vivo radiation and ex vivo radiation at breast-tissue level, and the effect of breast shielding on image quality. Materials and Methods The study was approved by institutional review and commpliant with HIPAA guidelines. Adult women who underwent 64-section coronary computed tomographic (CT) angiography and who provided informed consent were prospectively randomized to the use (n = 50) or absence (n = 51) of bismuth breast shields. Peripheral blood samples were obtained before and 30 minutes after in vivo radiation during CT angiography to compare DNA double-strand-break levels by γ-H2AX immunofluorescence in blood lymphocytes. To estimate DNA double-strand-break induction at breast-tissue level, a blood sample was taped to the sternum for ex vivo radiation with or without shielding. Data were analyzed by linear regression and independent sample t tests. Results Breast shielding had no effect on DNA double-strand-break levels from ex vivo radiation of blood samples under shields at breast-tissue level (unadjusted regression: β = .08; P = .43 versus no shielding), or in vivo radiation of circulating lymphocytes (β = -.07; P = .50). Predictors of increased DNA double-strand-break levels included total radiation dose, increasing tube potential, and tube current (P < .05). With current radiation exposures (median, 3.4 mSv), breast shielding yielded a 33% increase in image noise and 19% decrease in the rate of excellent quality ratings. Conclusion Among women who underwent coronary CT angiography, breast shielding had no effect on DNA double-strand-break levels in blood lymphocytes exposed to in vivo radiation, or ex vivo radiation at breast-tissue level. At present relatively low radiation exposures, breast shielding contributed to an increase in image noise and a decline in image quality. The findings support efforts to minimize radiation by primarily optimizing CT settings. (©) RSNA, 2016 Clinical trial registration no. NCT02617888 Online supplemental material is available for this article.

Optimisation of direct digital chest radiography using Cu filtration

PurposeTo assess the impact of additional Cu filtration with the DRX-1 detector in optimisation of adult chest radiography. MethodsA simulated anatomical phantom was imaged at different levels of Cu filtration (0 mm, 0.1 mm, 0.3 mm and 0.5 mm of Cu) at 120 kVp. Image quality was assessed using exposure index, conspicuity index, signal-to-noise ratio and pixel density. Dose area product (DAP) meter was used to measure dose; effective dose and risk of exposure induced cancer death (REID) were estimated using the PCXMC software 2.0. ResultsExposure index increased with increasing Cu thickness; 0.3 mm Cu filtration increased conspicuity index of simulated nodules, with a minimal decrease in signal-to-noise ratio, and pronounced decrease in pixel density. Patient doses were reduced by 20.7%, 36.6%, and 39.79% at 0.1 mm, 0.3 mm, and 0.5 mm levels of Cu filtration respectively. ConclusionAdditional beam filtration using 0.3 mm Cu filter with the DXR-1 detector reduced patient doses by 37% without decline in image quality.

Effect of reduction of the pulse rates of fluoroscopy and CINE-acquisition on x-ray dose and angiographic image quality during invasive cardiovascular procedures.

Reducing digital pulse rates (PR) are known to reduce total energy during invasive cardiovascular procedures, which likely has benefits for patients and staff. Physicians may be reluctant to reduce these parameters because they fear a decline in image quality that could affect procedural outcomes. We sought to assess the effect of default rates of fluoroscopy (Fluoro) and CINE-acquisition (CINE) on total x-ray dose and image quality during invasive cardiovascular procedures. We retrospectively reviewed procedures done with 2 default PRs: a standard dose cohort (PR, 15 for Fluoro and CINE), and a reduced dose cohort (PR, 10 for Fluoro and CINE). Total x-ray dose, Fluoro time, and contrast use were compared between groups. A blinded angiographic image quality assessment was then performed using an objective 10-point angiographic quality score. There were no significant differences between cohorts for fluoroscopy time or contrast use. The reduced dose cohort has a significant reduction in mean total x-ray dose (PR 15, 1763.1 mGy; PR 10, 1179.1 mGy; P<0.0001). When adjusted for potential confounders, a 38% reduction in total x-ray dose was identified (P<0.0001). There was no difference in adjusted angiographic quality score between the cohorts (PR 15, 7.90; PR 10, 8.00; P=0.67), indicating no decline in image quality with PR reduction. Reducing default PRs during invasive cardiovascular procedures yields large and significant reductions in total x-ray energy with no decline in angiographic image quality.

全景式TDICCD摆扫航空相机像面旋转的高精度补偿

In the imaging process of scanning camera, measures must be taken to guarantee the synchronous scanning movement between scanning mechanism and the detector of image plane. If there exists angular deviation or speed deviation between the two mechanisms, it gives rise to the image rotation phenomenon. The image is blurred, causing the decline of image quality. The paper takes the panoramic time delay and integration (TDI) CCD scanning aerial camera as an example, establishes the mathematical model of image surface rotation analysis using coordinate transformation method, analyzes the influences of position and speed synchronization error on imaging quality. The synchronous compensation method based on disturbance observer is presented, which ensures the position and speed synchronization between the two mechanisms, realizes the independent design of speed controller and synchronous controller and facilitates the practical application. Through the laboratory static resolution imaging and external imaging experiments, theoretical analysis results are verified. The experimental results show that the position and speed synchronous errors based on the method are less than 0.0043, 0.0695/s, respectively, which meet the requirements of compensation accuracy, in the imaging process the image rotation and distortion have been significantly inhibited and the image quality is greatly improved.

Application of Adaptive Enhancement Means in Head and Neck CR Image

Digital CR of head and neck overcomes the disadvantage of regular X-ray radiography, which can not reveal bone and soft tissue position deficiency in one exposing, and reduces the X-ray radiation dose. Meanwhile, various factors cause the decline of image quality, and images must be enhanced in order to meet demands of doctor's clinical diagnosis. The general enhancement algorithms don’t consider body's structure differences and density characteristics. A new adaptive CR enhancement algorithm was proposed in this article, and head and neck CR images were processed with this method and compared with linear unsharp masking method. The experiment proves that the details of CR image enhanced were abundant and enhanced CR image had good visual effect, SNR was high, as well as detail variance /background variance (DV/BV) indicating that this algorithm is suitable for head and neck CR medical images