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Automatic Quality Correction Algorithm Design for Transmission Line Images Based on the Saliency Model
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In this paper, a two-step image enhancement is presented. In the first step, color correction and underwater image quality enhancement are conducted if there are artifacts such as darkening, hazing and fogging. In the second step, the image resolution optimized in the previous step is enhanced using the convolutional neural network (CNN) with deep learning capability. The main reason behind the adoption of this two-step technique, which includes image quality enhancement and super-resolution, is the need for a robust strategy to visually improve underwater images at different depths and under diverse artifact conditions. The effectiveness and robustness of the real-time algorithm are satisfactory for various underwater images under different conditions, and several experiments have been undertaken for the two datasets of images. In both stages and for each of image datasets, the mean square error (MSE), peak signal to noise ratio (PSNR), and structural similarity (SSIM) evaluation measures were fulfilled. In addition, the low computational complexity and suitable outputs were obtained for different artifacts that represented divergent depths of water to achieve a real-time system. The super-resolution in the proposed structure for medium layers can offer a proper response. For this reason, time is also one of the major factors reported in the research. Applying this model to underwater imagery systems will yield more accurate and detailed information.
Image segmentation is an important task in image processing. However, no universally accepted quality scheme exists for evaluating the performance of various segmentation algorithms or just different parameterizations of the same algorithm. In this paper, an extension of a fusion-based framework for evaluating image segmentation quality is proposed. This framework uses supervised image segmentation evaluation measures as features. These features are combined together and used to train and test a number of classifiers. Preliminary results for this framework, using seven evaluation measures, were reported with an accuracy rate of 80%. In this study, ten image segmentation evaluation measures are used, nine of which have already been proposed in literature. Moreover, one novel measure is proposed, based on the Discrete Cosine Transform (DCT), and is thus named the DCT metric. Before applying it in the fusion-based framework, the DCT metric is first compared with some state-of-the-art evaluation measures. Experimental results demonstrate that the DCT metric outperforms some existing measures. The extended fusion-based framework for image segmentation evaluation proposed in the study outperforms the original fusion-based framework, with an accuracy rate of 86% and a large Kappa value equal to 0.72. Hence, the novelty in this paper is in two aspects: firstly, the DCT metric and secondly, the extension of the fusion-based framework for evaluation of image segmentation quality.
Non-uniform illumination images are of limited visibility due to under-exposure, over-exposure, or a combination of these two factors. Enhancing these images is a very challenging task in image processing. Although there are numerous enhancement methods to improve the visual quality of images, many of these methods produce undesirable results with regard to contrast and saturation improvements. In order to improve the visibility of images without over-enhancement or under-enhancement, a variational-based fusion method is proposed for adaptively enhancing non-uniform illumination images. First, a hue-preserving global contrast adaptive enhancement algorithm obtains a globally enhanced image. Second, a hue-preserving local contrast adaptive enhancement method produces a locally enhanced image. Finally, an enhanced result is obtained by a variational-based fusion model with contrast optimization and color correction. The final result represents a trade-off between global contrast and local contrast, and also maintains the color balance between the globally enhanced image and the locally enhanced image. This method produces visually desirable images in terms of contrast and saturation improvements. Experiments were conducted on a dataset that included different kinds of non-uniform illumination images. The results demonstrate that the proposed method outperforms the compared enhancement algorithms both qualitatively and quantitatively.
Purpose
To investigate the feasibility of reducing radiation dose by modifying tube voltage, window settings, and algorithm while maintaining image quality, based on the qualitative evaluation of its quality and the radiation dose, using raw data acquired in chest high-resolution computed tomography (HRCT).
Methods
Radiation exposure was measured using a Fluke dosimeter while modifying the tube voltage to 80 and 100 from 120 kVp in a 64-slice multi-detector computed tomography for comparison and analysis. Changes in image quality as a result of the different tube voltage settings, 3 different window settings (−550, −600, and −700), and 2 algorithms (standard and edge) were analyzed using ImageJ.
Results
Relative to 120kVp, the dose decreased by approximately 67.8% and 36.9% at 80 and 100 kVp, respectively. Image quality assessment showed that changing the window setting to −700 (window level) after scanning with the tube voltage set at 100 kVp and applying the edge algorithm reduced the radiation dose while maintaining the image quality.
Conclusions
The findings are significant with respect to the reduction of scan dose in that they demonstrate how radiation exposure can be reduced in a clinical scenario by altering the settings on an existing HRCT apparatus. Additional clinical trials and image assessments should be conducted on human participants to confirm the feasibility of altering HRCT settings for reducing scan doses.
To objectively and justly compare the results of remote-sensing image fusion, evaluate the fusion algorithm, and optimize the fusion process, a quality evaluation index system is constructed by considering both qualitative and quantitative evaluations. The quantitative evaluation indexes are divided into three types based on the analysis of evaluation methods of the fusion quality of primary remote-sensing images. The applicability and effectiveness of six pixel-level fusion methods of optical remote-sensing image fusion were verified based on the constructed quality evaluation index system used to evaluate those methods in offshore and archipelagic areas. The experimental results indicate that the constructed quality evaluation index system evaluates the fusion algorithm objectively, comprehensively, and accurately. The intensity-hue-saturation transformation-based method distorts spectral features of the original image and easily causes spectrum degradation, whereas the wavelet transformation-based method has an advantage in the preservation of spectral features but efficiently produces blockiness and image penumbrae. The principal component analysis transformation-based method preserves more detailed textures and structural characteristics of original images but loses some physical properties. The Brovey transformation-based method results in spectral distortion, the high pass filter-based method shows a clear boundary of surface features after image fusion, and the Gram-Schmidt-based method shows a high ability of spectral information retention, but the latter performs poorly in the preservation of spatial information. The proposed quality evaluation index system of remote-sensing image fusion can be used as the objective effect evaluation criteria for remote-sensing image fusion and offers a reference for future applications of fusion results. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).
A quest for a suitable method to fabricate thin films-based humidity sensor with superior sensitivity in their electric response, calls for a selection of an appropriate material and effective fabrication technique. In this paper, organic semiconductor metalloporphyrin (TDTBPPNi) has been utilized to fabricate highly sensitive humidity sensors by using a
novel eco-benign microwave-assisted synthetic procedure. Metalloporphyrin (TDTBPPNi) has been later deposited through facile drop-casting technique on the gap (40 μm) between planar metallic aluminium (Al) electrodes to fabricate surface-type capacitive and resistive type relative humidity sensor (Al/TDTBPPNi/Al). The structural and morphological characterizations of humidity sensing layer have been investigated, which indicate amorphous structure and rough globular surface morphology of the thin film, respectively. The relative humidity sensing capacitive and resistive characteristics of the sensor have been monitored in 39–85% relative humidity (%RH) bandwidth. The fabricated sensor under biasing condition of 1 V of applied bias (Vrms) and 200 Hz AC test frequency, exhibits significantly higher sensitivity of *102.61 pF/%RH and −333.07 kΩ/%RH in capacitive and resistive mode of operation. The average values of response
and reset time of resistive sensor have been estimated to be *35 and *57 s, respectively. The reasons for this achieved sensitivity and response level have also been discussed.
Tomographic Gamma Scanning (TGS) is a nondestructive analysis technique for accurately measuring the content of radioactive material in sealed containers. It has important applications in the fields of nuclear safety assurance and the measurement and analysis of radioactive waste drums. As the key of TGS technique, the quality of the emission image reconstruction algorithm will directly affect the accuracy of measurement results. In this paper, a computer simulation platform for TGS technique is established. The application of four emission image reconstruction algorithms in TGS technique is systematically studied by using 6 · 6 · 6 sample voxel model. Three representative sample media and radioactive source distribution models are established and the applicability of the four reconstruction algorithms is analyzed and evaluated in the presence and absence of detector noise interference. Computer simulation and experimental results show that ML-EM algorithm is the most applicable in the process of TGS image reconstruction.
There are various applied electro-optical devices, which utilize light emitting didoe (LED) chip array for applications to displays and opto-electronic sensors. In those devices, it is the one of the critical technical issues to minimize uncertain fluctuations including optical power and optical density. Due to variation in operating environment of a device, those are not corrected precisely by controlling parameters based on simple relation between parameters and resultant abovementioned outputs. Therefore, there is essential need to correct outputs in real-time based on correction function generated from the consideration on various operation condition. In this article, we introduce an output correction method through reporting real-time image noise reduction in the application to electro-photography with LED print head. In the technology of LED print head, as differences in optical characteristics between each LED cause vertical image noise, it should be corrected in order to obtain images that are comparable or better in quality compared to those produced by the conventional laser scanning method. Even though it seems that the method used to obtain uniform light power from each LED can solve this problem, it does not work well for high-resolution printing. Therefore, a scan method involving correction by a printed and scanned pattern is introduced through this work. The scan method is composed of correction patterns to minimize printing noise by its shape, the correction algorithm to calculate the optimized value and the printing algorithm to control gray levels in real-time precisely. We believe that the developed correction method upgrades the printing quality of the LPH printer better than commercial printers. The developed correction method can also be applied to various application areas that use an array-type light source such as display systems and lighting systems.
Underwater images surfer from serious color deviation and blurring due to the effects of light absorption and scattering. In this letter, a hybrid method, which includes color correction and underwater image dehazing, is proposed to improve the visual quality of degraded underwater images. Firstly, an efficient color correction algorithm is applied to remove color casts of underwater images. Then, underwater image dehazing method is proposed to improve the visibility of underwater images, which includes a global background light estimation algorithm specialized for underwater images and a medium transmission estimation algorithm based on the combination a regression model with the characteristics of light traveling in water medium. Since there is no available dataset in this relatively new research area, a dataset which includes 45 underwater images with a wide variety of contents is collected. Subjective and objective performance evaluations demonstrate that the proposed method significantly improves both color and visibility of degraded underwater images, and is comparable to and even outperforms several state-of-the-art methods.
In this paper, a novel shift-invariant dual-tree complex shearlet transform (SIDCST) is constructed and applied to infrared and visible image fusion. Firstly, the mathematical morphology is used for the source images. Then, the images are decomposed by SIDCST to obtain the low frequency sub-band coefficients and high frequency sub-band coefficients. For the low frequency sub-band coefficients, a novel sparse representation (SR)-based fusion rule is presented. For the high frequency sub-band coefficients, a scheme based on the theory of adaptive dual-channel pulse coupled neural network (2APCNN) is presented, and the energy of edge is used for the external input of 2APCNN. Finally, the fused image is obtained by performing the inverse SIDCST. The experimental results show that the proposed approach can obtain state-of-the-art performance compared with conventional image fusion methods in terms of both objective evaluation criteria and visual quality.
Selection of route is the most important aspect in the design of power transmission line and directly affects the total project cost and the reliability of operation. The model of global optimal route searching was constructed according to the characteristic of environment and the connective graph was processed. Then, the selection strategy and the pheromone updating method of the basic ant colony algorithm (ACA) was improved. For some content, the influencing factors on the choice of the optimal route such as geological conditions, hydrological conditions and forest land were added to the new algorithm, and the improved ACA was applied directly to search the global optimal route on the model constructed above. Simulation results showed that the method can exactly search out the global optimal route actually.
Geometric Calibration and Image Quality Assessment of High-Resolution Dual-Camera Satellite
- Zhou Fang
- Xinrong Wang
- Wei Ji
- Meng Xu
- Yinan Zhang
- Yan Li
- Longfei Li
- Fang Zhou