- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Can adaptive post-processing of storage phosphor plate panoramic radiographs provide better image quality? A comparison of anatomical image quality of panoramic radiographs before and after adaptive processing
摘要： Objective: The objective of the present study was to study the effect of adaptive image processing on the visibility of anatomical structures in storage phosphor plate (SPP) panoramic images. Materials and methods: Three hundred SPP panoramic X-ray radiographs of children and adolescents were used. The radiographs were post-processed using general operator processor (GOP) technology, resulting in both a standard-processed and a GOP-processed radiograph. Four specialists in dental radiology compared the structural image quality of all standard-processed and GOP-processed panorama images for six anatomical structures, using a six-point scale for visual grading characteristics (VGC) analysis. Results: For three of the anatomic structures – the root canal space of the mandibular left first premolar, mandibular canal left side and periodontal ligament space of the mandibular right first molar – there was a statistically significant difference to the GOP’s advantage. For the three remaining structures – dentino-enamel junction of the maxillary right first molar, crista alveolaris of the mandibular left molar area and floor of maxillary sinus right side – no significant difference between standard processing and GOP processing was obtained. Conclusions: The study demonstrates that it is possible to improve the quality of SPP radiographs and the visibility of anatomical structures by using the GOP technique. Manufacturers’ image-processing programs can be further developed, as there is a possibility of improving the diagnostic content of an image with external processing.
关键词： digital,storage phosphor plate,radiographic image enhancement,radiography,panoramic,Adaptive processing,dental
更新于2025-09-23 15:22:29
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A Pipeline Neural Network For Low-Light Image Enhancement
摘要： Low-light image enhancement is an important challenge in computer vision. Most of low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). Firstly, we show that Multi Scale Retinex (MSR) can be considered as a convolutional neural network (CNN) with Gaussian convolution kernel and blending the result of DWT can improve the image produced by MSR. Secondly, we propose our pipeline neural network, consisting of denoising net and low light image enhancement net (LLIE-net) which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in synthetic dataset and public dataset. Experiments reveal that in comparison with other state-of-the-art methods, our methods achieve better performance in the perspective of qualitative and quantitative analysis.
关键词： Convolutional Neural Network,LLIE-Net,Low-light image enhancement
更新于2025-09-23 15:22:29
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Color Image Enhancement Method with Variable Emphasis Degree
摘要： In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.
关键词： contrast,color image enhancement,differential gray-level histogram equalization,intensity saturation,colorfulness
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) - Cleveland, OH, USA (2018.10.17-2018.10.19)] 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) - Image Enhancement Method Based on Adaptive Fraction Gamma Transformation and Color Restoration for Wireless Capsule Endoscopy
摘要： Wireless capsule endoscopy (WCE) plays an important role in gastrointestinal (GI) tract observation and disease diagnosis. However, due to the limited illumination and complex environment in GI tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. Although many algorithms have been proposed to improve image quality from different aspects, the balance of algorithm complexity and image quality is still a challenge worth exploring. In this paper, we propose a novel function named adaptive fraction gamma transformation (AFGT) to enhance the quality of images captured by the WCE. Characteristics of AFGT are adaptive to different luminance in an image and self-tunable to the statistical characteristics of different images. Besides, HSI color space is adopted for color restoration, namely, the saturation component is linearly scaled according to the enhanced intensity component. Experimental results show that performances of the proposed method in both visual quality and objective assessments are better than that of the state-of-art methods.
关键词： wireless capsule endoscopy,color restoration,fraction gamma transformation,image enhancement
更新于2025-09-23 15:22:29
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Single-image low-light enhancement via generating and fusing multiple sources
摘要： Imperfect lightness conditions usually lower the visual quality of an image by bringing in unclear image details and poor image contrast. Traditional low-light enhancement models based on one single input are often limited in avoiding the effect of over-enhancement or under-enhancement. Models based on fusing multiple input sources usually perform well in relieving this issue, as they can harmonize the complementary visual appearances of a same scene provided by different sources. Nevertheless, these models still have difficulty in dealing with the situation that only one input is at hand, which usually happens in many practical situations. In this paper, we propose a low-light enhancement model that artificially enriches input sources and then seamlessly fuses them. Specifically, with an input image, we first generate multiple enhanced images based on a lightness-aware camera response model. These images are then fused at mid-level based on a patch-based image decomposition model. To validate our model, we conduct qualitative and quantitative comparisons with several state-of-the-art single-source and multi-source models on a collection of real-world images. Experimental results show that our model better improves the image quality in terms of visual naturalness and aesthetics.
关键词： Fusion,Image enhancement,Low-light image
更新于2025-09-23 15:21:21
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Lower Power, Better Uniformity, and Stability CBRAM Enabled by Graphene Nanohole Interface Engineering
摘要： With the steadily increasing spatial resolution of synthetic aperture radar images, the need for a consistent but locally adaptive image enhancement rises considerably. Numerous studies already showed that adaptive multilooking, able to adjust the degree of smoothing locally to the size of the targets, is superior to uniform multilooking. This study introduces a novel approach of multiscale and multidirectional multilooking based on intensity images exclusively but applicable to an arbitrary number of image layers. A set of 2-D circular and elliptical filter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coefficient domain where each coefficient measures the existence of Schmittlet-like structures in the image. By estimating their significance via the perturbation-based noise model, the best-fitting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-fitting Schmittlets is utilized to consistently enhance further image layers, e.g., multipolarized, multitemporal, or multifrequency layers, and on the other hand, it provides an optimal description of spatial patterns valuable for further image analysis. The final validation proves the advantages of the Schmittlets over six contemporary speckle reduction techniques in six different categories (preservation of the mean intensity, equivalent number of looks, and preservation of edges and local curvature both in strength and in direction) by the help of four test sites on three resolution levels. The additional value of the Schmittlet index layer for automated image interpretation, although obvious, still is subject to further studies.
关键词： image reconstruction,image representations,Adaptive filters,image edge analysis,image enhancement,synthetic aperture radar (SAR),image analysis,digital filters
更新于2025-09-23 15:21:01
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MedGA: A novel evolutionary method for image enhancement in medical imaging systems
摘要： Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram equalization, bi-histogram equalization, encoding and decoding Gamma transformations, and sigmoid transformations. We show that MedGA considerably outperforms the other approaches in terms of signal and perceived image quality, while preserving the input mean brightness. MedGA may have a significant impact in real healthcare environments, representing an intelligent solution for Clinical Decision Support Systems in radiology practice for image enhancement, to visually assist physicians during their interactive decision-making tasks, as well as for the improvement of downstream automated processing pipelines in clinically useful measurements.
关键词： Medical imaging systems,Genetic Algorithms,Uterine fibroids,Magnetic resonance imaging,Bimodal image histogram,Image enhancement
更新于2025-09-23 15:21:01
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A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
摘要： Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At ?rst, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the ?nal improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast signi?cantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.
关键词： convolutional neural network,Retinex model,image enhancement,color model,batch normalization,feature learning
更新于2025-09-23 15:21:01
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FPGA Implementation of Underwater Image Enhancement using Nonlinear Filtering
摘要： Background/Motivation: Statistical Analysis: they suffer from various adversary underwater conditions and require low power devices such as FPGA. Methods/ Optical image captured by autonomous underwater vehicles need to be preprocessed as Underwater image degradation due to non uniform illumination is corrected by non linear filtering and is implemented in FPGA. Two dimensional Fast Fourier Transform (FFT) and its inverse along with logarithmic computation were implemented in FPGA as a part of filter implementation. One dimensional row wise and column wise FPGA’s can be used for implementing FFT were computed first and then combined to form two dimensional FFT. architectures that require high level parallelism such as image processing algorithms due to its inherent parallelism. Frequency based filtering method which is employed for preprocessing an underwater image successfully provided good histogram compared to the original one. Device utilization for implementing structure was estimated. Novel method of FFT implementation, its inverse and logarithmic computation was used in this work. The method can be applied to digital signal processing applications that can be implemented in FPGA. Automation of image preprocessing is required in unmanned underwater vehicles used for cable detection, navigation etc. These vehicles are to be provided with low power devices such as FPGAs for long run in underwater applications.
关键词： Nonlinear Filtering,Fast Fourier Transform,FPGA,Automated Underwater Vehicles,Underwater Image Enhancement
更新于2025-09-23 15:21:01
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[IEEE 2019 Chinese Automation Congress (CAC) - Hangzhou, China (2019.11.22-2019.11.24)] 2019 Chinese Automation Congress (CAC) - Research of Low-Quality Laser Security Code Enhancement Technique
摘要： The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
关键词： Bilateral filter,Laser security code,Image enhancement,Unsharp masking
更新于2025-09-23 15:19:57