- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Dual-Core Photonic Crystal Fiber-Based Plasmonic RI Sensor in the Visible to Near-IR Operating Band
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： Adaptive filters,digital filters,image analysis,image reconstruction,image representations,image edge analysis,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
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Low-Light Image Enhancement Based on Nonsubsampled Shearlet Transform
摘要： To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST). The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details. An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST. Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients. Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement. Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.
关键词： Low-light image,nonsubsampled shearlet transform,image decomposition,noise suppression,image enhancement
更新于2025-09-19 17:13:59
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[IEEE 2019 Workshop on Recent Advances in Photonics (WRAP) - Guwahati, India (2019.12.13-2019.12.14)] 2019 Workshop on Recent Advances in Photonics (WRAP) - Hybrid Waveguide Platform for Integrated Photonics Application
摘要： A novel, fast, and practical way of enhancing images is introduced in this paper. Our approach builds on Laplacian operators of well-known edge-aware kernels, such as bilateral and nonlocal means, and extends these filter’s capabilities to perform more effective and fast image smoothing, sharpening, and tone manipulation. We propose an approximation of the Laplacian, which does not require normalization of the kernel weights. Multiple Laplacians of the affinity weights endow our method with progressive detail decomposition of the input image from fine to coarse scale. These image components are blended by a structure mask, which avoids noise/artifact magnification or detail loss in the output image. Contributions of the proposed method to existing image editing tools are: 1) low computational and memory requirements, making it appropriate for mobile device implementations (e.g., as a finish step in a camera pipeline); and 2) a range of filtering applications from detail enhancement to denoising with only a few control parameters, enabling the user to apply a combination of various (and even opposite) filtering effects.
关键词： image smoothing,local tone mapping,image editing,Image enhancement,image sharpening
更新于2025-09-16 10:30:52
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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - A Reflectance Based Method For Shadow Detection and Removal
摘要： Shadows are common aspect of images and when left undetected can hinder scene understanding and visual processing. We propose a simple yet effective approach based on reflectance to detect shadows from single image. An image is first segmented and based on the reflectance, illumination and texture characteristics, segments pairs are identified as shadow and non-shadow pairs. The proposed method is tested on two publicly available and widely used datasets. Our method achieves higher accuracy in detecting shadows compared to previous reported methods despite requiring fewer parameters. We also show results of shadow-free images by relighting the pixels in the detected shadow regions.
关键词： image enhancement,shadow removal,reflectance classifier,shadow detection
更新于2025-09-11 14:15:04
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy
摘要： Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.e. 1- Contrast Enhancement 2- Contrast-limited adaptive histogram equalization were separately embedded in the proposed layer and the results were compared. For identification of exudates, hemorrhages and microaneurysms, the proposed framework achieved the total accuracy of 87.6%, and 83.9% for the contrast enhancement and contrast-limited adaptive histogram equalization layers, respectively. However, the total accuracy of the convolutional neural network alone without the prreprocessing layer was found to be 81.4%. Consequently, the new convolutional neural network architecture with the proposed preprocessing layer improved the performance of convolutional neural network.
关键词： contrast-limited adaptive histogram equalization,contrast enhancement,preprocessing layer,diabetic retinopathy,Convolutional neural networks,image enhancement
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE International Conference on Multimedia and Expo (ICME) - San Diego, CA (2018.7.23-2018.7.27)] 2018 IEEE International Conference on Multimedia and Expo (ICME) - An Improved Guided Filtering Algorithm for Image Enhancement
摘要： Guided image filter (GIF) is popular in image processing and computer vision for the properties of edge-preserving and low computational complexity, but GIF may suffer from over-smoothing (halo artifacts) near sharp edges and under-smoothing at flat regions. There is a tradeoff between them in the original cost function of GIF. In this paper, an improved guided filter (IGIF) is proposed by incorporating an adaptive structure aware constraint. The adaptive structure aware constraint can well preserve edges and smooth details through assigning different weights to different local structure. Simultaneously, thanks to the L1 penalty, the proposed IGIF can exactly remove small details at the flat regions. To illustrate the effectiveness of the proposed IGIF, we apply it to image enhancement. Experimental results show that the proposed filter can produce enhanced images with better visual quality as well as quantitative performance.
关键词： adaptive structure aware constraint,image enhancement,Guided image filter
更新于2025-09-10 09:29:36
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Unified Image Fusion Framework with Learning-Based Application-Adaptive Importance Measure
摘要： This paper presents a novel unified image fusion framework based on an application-adaptive importance measure. In the proposed framework, an important area is selected using the importance measure obtained for each image type in each application. The key is to learn this application-adaptive importance measure that can select the important area irrespective of the input image type without manually designing the algorithm for each application. Then, the fused intensity is generated using Poisson image reconstruction. Experimental results demonstrate that the proposed framework is effective for various applications including depth-perceptible image enhancement, temperature-preserving image fusion, and haze removal.
关键词： RGBD,NIR,Image fusion,FIR,Image enhancement
更新于2025-09-10 09:29:36
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[ACM Press 2018 ACM Multimedia Conference - Seoul, Republic of Korea (2018.10.22-2018.10.26)] 2018 ACM Multimedia Conference on Multimedia Conference - MM '18 - Aesthetic-Driven Image Enhancement by Adversarial Learning
摘要： We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement.
关键词： Image Enhancement,Weakly-supervised Learning
更新于2025-09-10 09:29:36
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Dynamical stochastic resonance for non-uniform illumination image enhancement
摘要： Images taken under poor illumination conditions have low contrast and dark tones. General dark image enhancement algorithms cannot effectively enhance these images without introducing over-enhancement, detail loss, and noise amplification. In this study, a simple and fast enhancement technique of non-uniform illumination images is proposed based on dynamical stochastic resonance (DSR). The low-contrast images are enhanced through the nonlinear iteration by solving monostable Langevin equation. Iteration parameters are dynamically adjusted according to the intensity distribution of the original images, which ensure the balance of visibility and naturalness in the entire areas. A threshold is defined to automatically confirm the optimal outputs. The enhanced image is obtained by fusing the DSR result, original component, and illumination compensation component. The computational time, no-reference perceptual quality assessment, and lightness order error are measured to evaluate the performance of experimental results. The subjective and objective comparison with state-of-the-art methods shows that our method performs well to enhance the non-uniform illumination images with a low-computational complexity.
关键词： non-uniform illumination,Langevin equation,nonlinear iteration,image enhancement,dynamical stochastic resonance
更新于2025-09-10 09:29:36
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[ACM Press the 4th International Conference - Hong Kong, Hong Kong (2018.02.24-2018.02.26)] Proceedings of the 4th International Conference on Virtual Reality - ICVR 2018 - Light Stretch Algorithm for Image Quality Enhancement
摘要： Images taken from outdoor scenes are usually degraded due to weather condition, resulting in reduction in color fidelity as well as contrast of captured images. This gives rise to difficulties when processing these images by an algorithm designed for clear weather condition. In lieu of refining individual algorithms to be bad weather-appropriate, it sounds more reasonable to perform on each input image an enhancement step. Thus, image quality enhancement is currently in great demand. In this paper, we hereby propose the light stretch algorithm, which simultaneously performs intensity expansion and reduction by solving the saturation phenomenon of bright color region and the discoloration of dark color region in an image. The simulation result strengthens its superiority over the other two methods based on unidirectional image enhancement and Photoshop curve adjustment method.
关键词： symmetrical curve,light stretch,crossing curve,quadric curve,Image enhancement
更新于2025-09-10 09:29:36