- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Noise-Free Rule-Based Fuzzy Image Enhancement
摘要： Different kinds of noises have considerable effects on most of image sensing systems. Suitable image contrast enhancement algorithms can improve contrast or retain detail information while reducing noises as well. Fuzzy representation of an image provides a reliable analysis when inexactness occurred at the gray level values. This paper presents a fuzzy-based novel image contrast enhancement method. Several image quality indices, such as similarity, naturalness, and mean brightness preserving examined and experimentally show the effectiveness of the proposed technique in comparison with well-known image enhancement methods such as histogram equalization and contrast limited adaptive histogram equalization methods.
关键词： Image Robustness and Naturalness,Image Quality Index,Image Enhancement,Fuzzy System
更新于2025-09-23 15:23:52
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FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks
摘要： In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we proposed an image fusion algorithm using convolutional neural networks (FusionCNN). The fusion model implicitly represents a fusion rule whose inputs are a pair of source images and the output is a fused image with end-to-end property. As no datasets can be used to train FusionCNN in remote sensing field, we constructed a new dataset from a natural image set to approximate MS and Pan images. In order to obtain higher fusion quality, low frequency information of MS is used to enhance the Pan image in the pre-processing step. The method proposed in this paper overcomes the shortcomings of the traditional fusion methods in which the fusion rules are artificially formulated, because it learns an adaptive strong robust fusion function through a large amount of training data. In this paper, Landsat and Quickbird satellite data are used to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion algorithm is superior to the comparative algorithms in terms of both subjective and objective evaluation.
关键词： Convolutional neural networks,Deep learning,Remote sensing image fusion,Image enhancement
更新于2025-09-23 15:23:52
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[Communications in Computer and Information Science] Advances in Computing and Data Sciences Volume 905 (Second International Conference, ICACDS 2018, Dehradun, India, April 20-21, 2018, Revised Selected Papers, Part I) || Two Stage Histogram Enhancement Schemes to Improve Visual Quality of Fundus Images
摘要： A fundus image plays a signi?cant role to analyze a wide variety of ophthalmic conditions. One of the major challenges faced by ophthalmologist in the analysis of fundus images is its low contrast nature. In this paper, two stage histogram enhancement schemes to improve the visual quality of fundus images are proposed. Fuzzy logic and Histogram Based Enhancement algorithm (FHBE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm are cascaded one after the other to accomplish the two stage enhancement task. This results in two new enhancement schemes, namely FHBE-CLAHE and CLAHE-FHBE. The analysis of the results based on its visual quality shows that two stage enhancement schemes outperforms individual enhancement schemes.
关键词： Fundus images,FHBE,CLAHE,Image enhancement
更新于2025-09-23 15:23:52
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[IEEE 2018 International Conference on Applied Engineering (ICAE) - Batam, Indonesia (2018.10.3-2018.10.4)] 2018 International Conference on Applied Engineering (ICAE) - Vision Based Flame Detection System For Surveillance Camera
摘要： Flame detection systems are useful for the development of fire early detection systems. In general, conventional fire detection system which is usually used heat detector cannot detect the presence of fire since the detector requires much time to detect temperature changes. The use of a camera as an image scanner sensor has the potential to be used as an identifier of a flame shape or color. This study aims to build an image processing flame detection system using image enhancement, segmentation and filtering methods which is applied the Vission Assistant available in LabVIEW. The system implemented in a frame rate of 30 fps with a resolution of 1024 x 768, in which the system accuracy reach up to 98%.
关键词： Image Enhancement,Color Filtering,LabVIEW,HSV Filtering,Flame Detection
更新于2025-09-23 15:23:52
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Image Enhancement Using Patch-Based Principal Energy Analysis
摘要： The visual quality of a captured image is often degraded by complicated lighting conditions in various real-world environments. This quality deterioration probably leads to the significant performance drop in many algorithms of computer vision, which require high-visibility inputs for precise results. In this paper, a novel method for image enhancement is proposed with the principal energy analysis. Specifically, based on the key observation that the illumination component is dominant over a small local region, the corresponding energy is efficiently separated from the scene reflectance by exploiting the subspace analysis. Owing to this clear separation, the illumination component can be easily adjusted independent of the reflectance layer for better visual aesthetics. In contrast to previous methods that still suffer from the exaggerated or conservative restoration yielding the loss of details and defects of halo artifacts, the proposed scheme has a good ability to enhance the image contrast while successfully preserving the color attribute of the original scene. Moreover, the proposed method is conceptually simple and easy to implement. Experimental results demonstrate the effectiveness of the proposed method even under diverse lighting conditions, e.g., low light, casting shadow, uneven illuminations, and so on, and the superiority of the proposed method over previous approaches introduced in the literature.
关键词： Quality deterioration,principal energy analysis,subspace analysis,illumination component,image enhancement
更新于2025-09-23 15:23:52
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An Efficient Approach to Restore Naturalness of Non-uniform Illumination Images
摘要： Enhancement of the image details without affecting the naturalness is a difficult task, especially for non-uniformly illuminated images. While dealing with non-uniformly illuminated images, most of the available image enhancement approaches show common drawbacks such as loss of naturalness and appearance of artifacts in the resultant image. It is very difficult to maintain a trade-off between detail enhancement and naturalness. To deal with this problem, we propose an efficient approach for enhancing local details as well as the color information and preserve the naturalness in the resultant image. The proposed method is effectively enhancing the local details, along with the visibility of the image (having dark and bright regions) without affecting the naturalness. Experimental results also support our claims and confirmations that the proposed approach outperforms other state-of-the-art methods.
关键词： Color preservation,Naturalness preservation,Image enhancement,Contrast enhancement
更新于2025-09-23 15:22:29
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Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways
摘要： Due to the cost limitation of camera sensors, images captured in low-light environments often suffer from low contrast and multiple types of noise. A number of algorithms have been proposed to improve contrast and suppress noise in the input low-light images. In this paper, a deep refinement network, LL-RefineNet, is built to learn from the synthetical dark and noisy training images, and perform image enhancement for natural low-light images in symmetric—forward and backward—pathways. The proposed network utilizes all the useful information from the down-sampling path to produce the high-resolution enhancement result, where global features captured from deeper layers are gradually refined using local features generated by earlier convolutions. We further design the training loss for mixed noise reduction. The experimental results show that the proposed LL-RefineNet outperforms the comparative methods both qualitatively and quantitatively with fast processing speed on both synthetic and natural low-light image datasets.
关键词： deep refinement network,image enhancement,low-light image
更新于2025-09-23 15:22:29
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Empirical system of image enhancement for digital microscopic pneumonia bacteria images
摘要： In this paper the image processing method is used to enhance the pneumonia bacteria images. This paper recognized the bacteria images based on two domains. The enhancement techniques used for bacteria image enhancement were median filter, wiener filter, single scale retinex and multiscale retinex. Image enhancement has a very important role in digital image processing. The median and wiener filters were used for grayscale image enhancement. Then single scale retinex and multiscale retinex were used for color image enhancement. Based on performance metrics identified median filter is suitable for bacteria images in grayscale image enhancement and multiscale retinex is suitable for bacteria color image enhancement (Tab. 2, Fig. 8, Ref. 21). Text in PDF www.elis.sk.
关键词： image processing,median filter,multiscale retinex,image enhancement,wiener filter,single scale retinex,Pneumonia bacteria
更新于2025-09-23 15:22:29
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Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination
摘要： Underwater imaging is attractive but challenging. Images could be severely degraded by the particles in turbid water because of backscatter generation and signal light attenuation. In this paper, we focus on the scheme of underwater imaging and study the methods of visibility enhancement of turbid underwater images based on polarimetric imaging utilizing active unpolarized illumination. Compared with traditional polarimetric imaging using linearly polarized illumination, using unpolarized illumination ensures the polarization effect of the signal light could be neglected, no matter the depolarization degree of the object is high or low, which expands the application range of underwater polarimrteic imaging and makes the underwater polarimetric imaging scheme more reliable and robust. Experimentally, the visibility and the contrast of underwater images are enhanced effectively. In addition, it is demonstrated that our method is applicable for objects of different materials and different imaging distances in turbid water. The contrast of underwater images could be promoted at least 100%, meaning that this kind of technique can be potentially used in many underwater environments.
关键词： imaging through turbid media,polarimetric imaging,image enhancement
更新于2025-09-23 15:22:29
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[Lecture Notes in Computational Vision and Biomechanics] Computer Aided Intervention and Diagnostics in Clinical and Medical Images Volume 31 || A Review on Haze Removal Techniques
摘要： Images captured in outer environment suffer from reduced scene visibility, reduced color contrast, and increased color fading. This may happen due to the presence of haze, fog, smoke, dust and noise present in the outer atmosphere. Haze is formed due to two basic processes, attenuation and air light. Attenuation diminishes the contrast of the image and air light makes the image to be whiter and hence the image captured is unclear. Haze removal or image dehazing is required in real-world weather conditions to obtain a fast and high-quality hazy free image which is used in various ?elds like satellite systems and aircraft systems. The intention of this review paper is to give a brief analysis on different haze removal techniques.
关键词： Haze removal,Image enhancement,Dark channel prior,Genetic algorithm,Filtering
更新于2025-09-23 15:22:29