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1.
FPGA implementation of a real-time biologically inspired image enhancement algorithm 总被引:1,自引:0,他引:1
This paper presents an FPGA implementation of a novel image enhancement algorithm, which compensates for the under-/over-exposed
image regions, caused by the limited dynamic range of contemporary standard dynamic range image sensors. The algorithm, which
is motivated by the attributes of the shunting center-surround cells of the human visual system, is implemented in Altera
Stratix II GX: EP2SGX130GF1508C5 FPGA device. The proposed implementation, which is synthesized in an FPGA technology, employs
reconfigurable pipeline, structured memory management, and data reuse in spatial operations, to render in real-time the huge
amount of input data that the video signal comprises. It also avoids the use of computationally intensive operations, achieving
the required specifications in terms of flexibility, timing, performance and visual quality. The proposed implementation allows
real-time processing of color images with sizes up to 2.5 Mpixels, at frame rate of 25 fps. As a result, the architectural
solution described in this work offers a low-cost implementation for automatic exposure correction in real-time video systems.
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I. AndreadisEmail: |
2.
Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images. However it does not preserve the brightness and natural appearance of the images, which is a major drawback. To overcome this limitation, several Bi- and Multi-HE methods have been proposed. Although the Bi-HE methods significantly enhance the contrast and may preserve the brightness, the natural appearance of the images is not preserved as these methods suffer with the problem of intensity saturation. While Multi-HE methods are proposed to further maintain the brightness and natural appearance of images, but at the cost of contrast enhancement. In this paper, two novel Multi-HE methods for contrast enhancement of natural images, while preserving the brightness and natural appearance of the images, have been proposed. The technique involves decomposing the histogram of an input image into multiple segments based on mean or median values as thresholds. The narrow range segments are identified and are allocated full dynamic range before applying HE to each segment independently. Finally the combined equalized histogram is normalized to avoid the saturation of intensities and un-even distribution of bins. Simulation results show that, for the variety of test images (120 images) the proposed method enhances contrast while preserving brightness and natural appearance and outperforms contemporary methods both qualitatively and quantitatively. The statistical consistency of results has also been verified through ANOVA statistical tool. 相似文献
3.
Bo WANG Zitong KANG Pengwei DONG Fan WANG Peng MA Jiajing BAI Pengwei LIANG Chongyi LI 《Frontiers of Computer Science》2023,17(2):172702
Underwater images often exhibit severe color deviations and degraded visibility, which limits many practical applications in ocean engineering. Although extensive research has been conducted into underwater image enhancement, little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes. In this paper, we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters, which effectively removes color casts of a variety of underwater images. A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed, which circumvents the influence of white or bright regions that challenges existing physical model-based methods. To enhance contrast of resultant images, a piece-wise affine transform is applied to the transmission map estimated via background light differential. Finally, with the estimated background light and transmission map, the scene radiance is recovered by addressing an inverse problem of image formation model. Extensive experiments reveal that our results are characterized by natural appearance and genuine color, and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics, which further validates the better robustness and higher generalization ability of our enhancement model. 相似文献
4.
水体对于不同波长的光信号衰减程度不一致,这种现象破坏了水下图像的清晰度和色彩恒定性。为了解决水下图像亮度与色彩扭曲问题,提出一种基于同态滤波的水下图像增强与色彩校正模型。首先,通过比尔-朗伯定律和路径辐射分量构建出水下成像模型。其次,通过同态滤波对未经过衰减的水下图像进行估计。最后,通过麦克劳林级数对水下成像模型进行级数展开,进而推导出一种保持颜色恒定的水下图像色彩校正模型。实验部分分别对比了水下图像的主观视觉效果和客观评价指标,验证了该算法能够有效地保证水下图像的清晰度和色彩恒定性。校正后的水下图像细节丰富,色彩逼真。 相似文献
5.
Sergio Saponara Luca Fanucci Stefano Marsi Giovanni Ramponi 《Journal of Real-Time Image Processing》2007,1(4):267-283
This paper presents novel algorithmic and architectural solutions for real-time and power-efficient enhancement of images
and video sequences. A programmable class of Retinex-like filters, based on the separation of the illumination and reflectance
components, is proposed. The dynamic range of the input image is controlled by applying a suitable non-linear function to
the illumination, while the details are enhanced by processing the reflectance. An innovative spatially recursive rational
filter is used to estimate the illumination. Moreover, to improve the visual quality results of two-branch Retinex operators
when applied to videos, a novel three-branch technique is proposed which exploits both spatial and temporal filtering. Real-time
implementation is obtained by designing an Application Specific Instruction-set Processor (ASIP). Optimizations are addressed
at algorithmic and architectural levels. The former involves arithmetic accuracy definition and linearization of non-linear
operators; the latter includes customized instruction set, dedicated memory structure, adapted pipeline, bypasses, custom
address generator, and special looping structures. The ASIP is synthesized in standard-cells CMOS technology and its performances
are compared to known Digital signal processor (DSP) implementations of real-time Retinex filters. As a result of the comparison,
the proposed algorithmic/architectural design outperforms state-of-art Retinex-like operators achieving the best trade-off
between power consumption, flexibility, and visual quality.
Sergio Saponara is a Research Scientist and Assistant Professor at the University of Pisa. He was born in Bari, Italy, in 1975. He received the Electronic Engineering degree cum laude and the Ph.D. in Information Engineering, both from Pisa University, in 1999 and 2003, respectively. Since 2001 he collaborates with Consorzio Pisa Ricerche, Italy and in 2002 he was with IMEC, Belgium as Marie Curie research fellow. His research and teaching interests include electronic circuits and systems for multimedia, telecom and automation. He co-authored more than 40 papers including journals, conferences and patents. Luca Fanucci is Associate Professor of Microelectronics at the University of Pisa. He was born in Montecatini, Italy, in 1965. He received the Doctor Engineer degree and the Ph.D. in Electronic Engineering from the University of Pisa in 1992 and 1996, respectively. From 1992 to 1996, he was with the European Space Agency's Research and Technology Center, Noordwijk, The Netherlands, and from 1996 to 2004 he was a Research Scientist of the Italian National Research Council in Pisa. His research interests include design technologies for integrated circuits and systems, with emphasis on system-level design, hardware/software co-design and low-power. He co-authored more than 100 journal/conference papers and holds more than 10 patents. Stefano Marsi was born in Trieste, Italy, in 1963. He received the Doctor Engineer degree in Electronic Engineering (summa cum laude) in 1990 and the Ph.D. degree in 1994. Since 1995 he has held the position of researcher in the Department of Electronics at the University of Trieste where he is the teacher of courses in electronic field. His research interests include non-linear operators for image and video processing and their realization through application specific electronics circuits. He is author or co-author of more than 40 papers in international journals, proceedings of international conferences or contributions in books. Giovanni Ramponi is Professor of Electronics at the Department of Electronics of the University of Trieste, Italy. His research interests include nonlinear digital signal processing, and the enhancement and feature extraction in images and image sequences. Prof. Ramponi has been an Associate Editor of the IEEE Signal Processing Letters and of the IEEE Transactions on Image Processing; presently is an AE of the SPIE Journal of Electronic Imaging. He has participated in various EU and National Research Projects. He is the co-inventor of various pending international patents and has published more than 140 papers in international journals and conference proceedings, and as book chapters. Prof. Ramponi contributes to several undergraduate and graduate courses on digital signal processing. 相似文献
Giovanni RamponiEmail: |
Sergio Saponara is a Research Scientist and Assistant Professor at the University of Pisa. He was born in Bari, Italy, in 1975. He received the Electronic Engineering degree cum laude and the Ph.D. in Information Engineering, both from Pisa University, in 1999 and 2003, respectively. Since 2001 he collaborates with Consorzio Pisa Ricerche, Italy and in 2002 he was with IMEC, Belgium as Marie Curie research fellow. His research and teaching interests include electronic circuits and systems for multimedia, telecom and automation. He co-authored more than 40 papers including journals, conferences and patents. Luca Fanucci is Associate Professor of Microelectronics at the University of Pisa. He was born in Montecatini, Italy, in 1965. He received the Doctor Engineer degree and the Ph.D. in Electronic Engineering from the University of Pisa in 1992 and 1996, respectively. From 1992 to 1996, he was with the European Space Agency's Research and Technology Center, Noordwijk, The Netherlands, and from 1996 to 2004 he was a Research Scientist of the Italian National Research Council in Pisa. His research interests include design technologies for integrated circuits and systems, with emphasis on system-level design, hardware/software co-design and low-power. He co-authored more than 100 journal/conference papers and holds more than 10 patents. Stefano Marsi was born in Trieste, Italy, in 1963. He received the Doctor Engineer degree in Electronic Engineering (summa cum laude) in 1990 and the Ph.D. degree in 1994. Since 1995 he has held the position of researcher in the Department of Electronics at the University of Trieste where he is the teacher of courses in electronic field. His research interests include non-linear operators for image and video processing and their realization through application specific electronics circuits. He is author or co-author of more than 40 papers in international journals, proceedings of international conferences or contributions in books. Giovanni Ramponi is Professor of Electronics at the Department of Electronics of the University of Trieste, Italy. His research interests include nonlinear digital signal processing, and the enhancement and feature extraction in images and image sequences. Prof. Ramponi has been an Associate Editor of the IEEE Signal Processing Letters and of the IEEE Transactions on Image Processing; presently is an AE of the SPIE Journal of Electronic Imaging. He has participated in various EU and National Research Projects. He is the co-inventor of various pending international patents and has published more than 140 papers in international journals and conference proceedings, and as book chapters. Prof. Ramponi contributes to several undergraduate and graduate courses on digital signal processing. 相似文献
6.
目的 在沙尘天气条件下,由于大气中悬浮微粒对入射光线的吸收和散射,户外计算机视觉系统所采集图像通常存在颜色偏黄失真和低对比度等问题,严重影响户外计算机视觉系统的性能。为此,提出一种带色彩恢复的沙尘图像卷积神经网络增强方法,由一个色彩恢复子网和一个去尘增强子网组成。方法 采用提出的色彩恢复子网(sand dust color correction, SDCC)校正沙尘图像的偏色,将颜色校正后的图像作为条件,输入到由自适应实例归一化残差块组成的去尘增强子网中,对沙尘图像进行增强处理。本文还提出一种基于物理光学模型的沙尘图像合成方法,并采用该方法构建了大规模的配对沙尘图像数据集。结果 对大量沙尘图像的实验结果表明,所提出的沙尘图像增强方法能很好地去除图像中的偏色和沙尘,获得正常的视觉颜色和细节清晰的图像。进一步的对比实验表明,该方法能取得优于对比方法的增强图像。结论 本文所提出的沙尘图像增强方法能很好地消除整体的黄色色调和尘霾现象,获得正常的视觉色彩和细节清晰的图像。 相似文献
7.
LIMing 《中国科学:信息科学(英文版)》2010,(9):1913-1916
8.
Sergio Saponara Michele Casula Luca Fanucci 《Journal of Real-Time Image Processing》2008,3(3):201-216
To meet both flexibility and performance requirements, particularly when implementing high-end real-time image/video processing
algorithms, the paper proposes to combine the application specific instruction-set processor (ASIP) paradigm with the reconfigurable
hardware one. As case studies, the design of partially reconfigurable ASIP (r-ASIP) architectures is presented for two classes
of algorithms with widespread diffusion in image/video processing: motion estimation and retinex filtering. Design optimizations
are addressed at both algorithmic and architectural levels. Special processor concepts used to trade-off performance versus
flexibility and to enable new features of post-fabrication configurability are shown. Silicon implementation results are compared
to known ASIC, DSP or reconfigurable designs; the proposed r-ASIPs stand for their better performance–flexibility figures
in the respective algorithmic class.
Sergio Saponara got the Laurea degree, cum laude, and the Ph.D. in Electronic Engineering from the University of Pisa in 1999 and 2003, respectively. In 2002, he was with IMEC, Leuven (B), as Marie Curie Research Fellow. Since 2001, he collaborates with Consorzio Pisa Ricerche-TEAM in Pisa. He is senior researcher at the University of Pisa in the field of VLSI circuits and systems for telecom, multimedia, space and automotive applications. He is co-author of more than 80 scientific publications. He holds the chair of electronic systems for automotive and automation at the Faculty of Engineering. Michele Casula received the Laurea degree in Electronic Engineering from the University of Pisa in 2005. Since 2006, he is pursuing a Ph.D. degree in Information Engineering at the same university. His current interests involve VLSI circuits design, computer graphics, and Network-on-Chips. Luca Fanucci received the Laurea degree and the Ph.D. degree in Electronic Engineering from the University of Pisa in 1992 and 1996, respectively. From 1992 to 1996, he was with ESA/ESTEC, Noordwijk (NL), as a research fellow. From 1996 to 2004, he was a senior researcher of the Italian National Research Council in Pisa. He is Professor of Microelectronics at the University of Pisa. His research interests include design methodologies and hardware/software architectures for integrated circuits and systems. Prof. Fanucci has co-authored more than 100 scientific publications and he holds more than ten patents. 相似文献
Luca FanucciEmail: |
Sergio Saponara got the Laurea degree, cum laude, and the Ph.D. in Electronic Engineering from the University of Pisa in 1999 and 2003, respectively. In 2002, he was with IMEC, Leuven (B), as Marie Curie Research Fellow. Since 2001, he collaborates with Consorzio Pisa Ricerche-TEAM in Pisa. He is senior researcher at the University of Pisa in the field of VLSI circuits and systems for telecom, multimedia, space and automotive applications. He is co-author of more than 80 scientific publications. He holds the chair of electronic systems for automotive and automation at the Faculty of Engineering. Michele Casula received the Laurea degree in Electronic Engineering from the University of Pisa in 2005. Since 2006, he is pursuing a Ph.D. degree in Information Engineering at the same university. His current interests involve VLSI circuits design, computer graphics, and Network-on-Chips. Luca Fanucci received the Laurea degree and the Ph.D. degree in Electronic Engineering from the University of Pisa in 1992 and 1996, respectively. From 1992 to 1996, he was with ESA/ESTEC, Noordwijk (NL), as a research fellow. From 1996 to 2004, he was a senior researcher of the Italian National Research Council in Pisa. He is Professor of Microelectronics at the University of Pisa. His research interests include design methodologies and hardware/software architectures for integrated circuits and systems. Prof. Fanucci has co-authored more than 100 scientific publications and he holds more than ten patents. 相似文献
9.
David A. Bader Joseph Jájá David Harwood Larry S. Davis 《The Journal of supercomputing》1996,10(2):141-168
This paper presents efficient and portable implementations of a powerful image enhancement process, the Symmetric Neighborhood Filter (SNF), and an image segmentation technique that makes use of the SNF and a variant of the conventional connected components algorithm which we call -Connected Components. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. The image segmentation algorithm makes use of an efficient connected components algorithm based on a novel approach for parallel merging. The algorithms have been coded in Split-C and run on a variety of platforms, including the Thinking Machines CM-5, IBM SP-1 and SP-2, Cray Research T3D, Meiko Scientific CS-2, Intel Paragon, and workstation clusters. Our experimental results are consistent with the theoretical analysis (and provide the best known execution times for segmentation, even when compared with machine-specific implementations). Our test data include difficult images from the Landsat Thematic Mapper (TM) satellite data.Also affiliated with the Department of Electrical Engineering.Also affiliated with the Department of Computer Science and the Center for Automation Research. 相似文献
10.
Tamalika Chaira 《Applied Soft Computing》2012,12(4):1259-1266
This paper gives a novel scheme using intuitionistic fuzzy set theory to enhance the edges of medical images. Medical images contain lots of uncertainties, as they are poorly illuminated and fuzzy/vague in nature. So, direct segmentation techniques will not produce better results. There are lots of researches on edge enhancement starting from non-fuzzy to fuzzy set, but proper enhancement (highlighting important structures) is not obtained. Enhancement of edges helps in recovering the important structures that are not visible properly. Even minute pathological blood vessels/cells are not visible properly and in that case edge enhancement will enhance these blood vessels/cells. Intuitionistic fuzzy set theory is found suitable in medical image processing as it considers more (two) uncertainties as compared to fuzzy set theory. In the processing phase, image is initially converted to intuitionistic fuzzy image and intuitionistic fuzzy entropy is used to obtain the optimum value of the parameter in the membership and non-membership functions. Then it computes the total variation of the pixels with respect to the median value of the image window (rank order filtering). This enhances the borders or the edges of the image. The resulting image is then segmented (edge detected) using standard Canny's edge detector, when simply using Canny's edge detector does not give better result. From the result it is observed that on comparing with non-fuzzy and fuzzy methods, the proposed method gives better information about the images, which is helpful to the pathologists in accurate diagnosing of diseases. 相似文献