- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - Lviv (2018.9.11-2018.9.14)] 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) - A New Technique for Image Enhancement Based on the use of Mean Values for Subranges of Brightness
摘要： This paper discusses the problem of improving the quality of complex images in automatic mode with an acceptable level of computational costs. In this work, the task of improving the efficiency of contrast enhancement for monochrome images in the automatic mode is being solved. In this paper a new technique of contrast enhancement for complex monochrome images with a multi-modal distribution of brightness is proposed. The proposed technique is based on measuring the average values of brightness for subranges of the full range of brightness. The paper presents the results of research of various methods of increasing the contrast of an image using known metrics of integral contrast.
关键词： mean value,image enhancement,brightness subrange,contrast
更新于2025-09-23 15:19:57
-
[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - A New Database for Evaluating Underwater Image Processing Methods
摘要： In this paper, we present a new, large-scale database on underwater image, which is called the NWPU underwater image database. This database contains 6240 underwater images of 40 objects. Each object is captured with 6 different levels of turbidity water, 4 lighting conditions and 6 different distances. Among them, we use the underwater images with turbidity value of 0 as Ground-truth. In addition, we captured the shadowless image of the object in the air and clear water. Different from other underwater databases, we capture underwater images with real high turbidity lake water instead of simulating the turbidity of water. This method ensures that the underwater images we captured are as close as possible to the real environment. We have given the database baseline which contains multi-scale Retinex with color restore (MSRCR) algorithms for enhancing images and four commonly used image quality evaluation criteria, including two full-references and two no-references methods. The four image quality evaluation methods include two no-reference and two full reference.
关键词： turbidity,underwater image,image quality evaluation,image enhancement and restoration
更新于2025-09-19 17:15:36
-
[ACM Press the Thirteenth ACM International Conference - Shenzhen, China (2018.12.03-2018.12.05)] Proceedings of the Thirteenth ACM International Conference on Underwater Networks & Systems - WUWNet '18 - Optical imaging study of underwater acousto-optical fusion imaging systems
摘要： Existing ocean visualization studies are usually conducted independently using either sonar or optical imaging. However, the two methods have their own shortcomings in different engineering applications. Acoustic imaging is not comprehensive enough to show the details of the target, and the perspective of the optical imaging is not extensive enough. Combining the advantages of optics and acoustics, this paper proposes a joint imaging method with acoustic communication-assisted decision-making and optical image stitching, aiming to improve information acquisition efficiency in the ocean visualization process. The joint imaging method relies on sonar technology as the decision-making layer to obtain the position information, then it sends instructions to AUV by acoustic communication and get the details of the target by the AUV-mounted camera which forms the executive layer. Finally, it conducts smoothing and mosaic processing on the optical image. The system can efficiently obtain complete, comprehensive, and detailed ocean visualization information through the advantages of agility of acoustic and accuracy of optics.
关键词： image mosaic,acousto-optical fusion,Underwater imaging,image enhancement
更新于2025-09-19 17:15:36
-
[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - Adversarial Context Aggregation Network for Low-Light Image Enhancement
摘要： Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
关键词： context aggregation,Low-light image enhancement,Convolutional neural network,generative adversarial network
更新于2025-09-19 17:15:36
-
[IEEE 2018 International Conference on Frontiers of Information Technology (FIT) - Islamabad, Pakistan (2018.12.17-2018.12.19)] 2018 International Conference on Frontiers of Information Technology (FIT) - Color Image Enhancement Using Multiscale Retinex with Guided Filter
摘要： Image enhancement algorithms are developed to achieve improved image quality. This paper presents a novel technique to enhance colored images using Multi Scale Retinex (MSR) along with the Guided Filter (GF). The proposed technique has several steps. Initially, the PCA isolates the input RGB image into the luminance and chrominance channels. On luminance channel, the MSR is applied in three steps. Meanwhile, the GF is introduced, which divides the image into high and low frequency components. Finally, Contrast Stretching (CS) is applied that expands the contrast of the image. Proposed method yields visually pleasing images on NASA retinex dataset and outperforms few techniques in terms of the entropy, the PSNR, and memory usage. In addition, the proposed method is computationally efficient.
关键词： Multi Scale Retinex,Image Enhancement,Guided Filter
更新于2025-09-19 17:15:36
-
Detail-preserving underexposed image enhancement via optimal weighted multi-exposure fusion
摘要： Underexposed image enhancement aims at revealing hidden details that are barely noticeable in underexposed images due to low light conditions. Previous work may inevitably wash out some weak edges and lose details when handling several underexposed images. To deal with these problems, this paper presents a detail-preserving underexposed image enhancement method based on a new optimal weighted multi-exposure fusion mechanism. Providing an input underexposed image, we propose a novel multi-exposure image enhancement method which can generate a multi-exposure image sequence. However, none of these images are good enough, as images with high exposure have good brightness and color information, whereas sharp details are better preserved in the images with lower exposure. In order to preserve details and enhance the blurred edges, we propose to solve an energy function to compute the optimal weight of the three measurements: local contrast, saturation, and exposedness. Then a weighted multi-exposed fusion method is used to generate the final image. Since the proposed approach is computationally light-weight, it is possible to implement it on mobile devices such as smart phones and compact cameras. Various experiment results validate our new method.
关键词： Underexposed image enhancement,weighted multi-exposure fusion,detail preserving
更新于2025-09-19 17:15:36
-
Reliable and Accurate Wheel Size Measurement under Highly Reflective Conditions
摘要： Structured-light vision sensor, as an important tool for obtaining 3D data, is widely used in fields of online high-precision measurement. However, the captured stripe images can show high-dynamic characteristics of low signal-to-noise ratio and uneven brightness due to the complexity of the onsite environment. These conditions seriously affect measurement reliability and accuracy. In this study, a wheel size measurement framework based on a structured-light vision sensor, which has high precision and reliability and is suitable for highly reflective conditions, is proposed. Initially, the quality evaluation criterion of stripe images is established, and the entire stripe is distinguished into high- and low-quality segments. In addition, the multi-scale Retinex theory is adopted to enhance stripe brightness, which improves the reliability of subsequent stripe center extraction. Experiments verify that this approach can remarkably improve measurement reliability and accuracy and has important practical value.
关键词： stripe image enhancement,wheel size measurement,reflective conditions,High dynamic
更新于2025-09-19 17:15:36
-
Low-Complexity Power-Balancing-Point Based Optimization for Photovoltaic Differential Power Processing
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 ?lter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coef?cient domain where each coef?cient measures the existence of Schmittlet-like structures in the image. By estimating their signi?cance via the perturbation-based noise model, the best-?tting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-?tting 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 ?nal 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 analysis,image reconstruction,image representations,image edge analysis,digital ?lters,Adaptive ?lters,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - CAOL 2019 Cover Page
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 ?lter kernels in different scales and orientations (named Schmittlets) is derived from hyperbolic functions. The original intensity image is transformed into the Schmittlet coef?cient domain where each coef?cient measures the existence of Schmittlet-like structures in the image. By estimating their signi?cance via the perturbation-based noise model, the best-?tting Schmittlets are selected for image reconstruction. On the one hand, the index image indicating the locally best-?tting 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 ?nal 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 analysis,image reconstruction,image representations,image edge analysis,digital ?lters,Adaptive ?lters,image enhancement,synthetic aperture radar (SAR)
更新于2025-09-19 17:13:59
-
A Phase Calibration Method for Millimeter-Wave Up-Converter Using Electro-Optic Sampling
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