1.上海大学 微电子研究与开发中心,上海 200444
2.上海大学 机电工程与自动化学院,上海 200444
3.上海大学 上海电影学院,上海 200072
扫 描 看 全 文
JI Yuan, LI Xingyi, MA Xinde, et al. Improved Retinex low-light image enhancement for stage scenes. [J]. Optics and Precision Engineering 31(17):2573-2583(2023)
JI Yuan, LI Xingyi, MA Xinde, et al. Improved Retinex low-light image enhancement for stage scenes. [J]. Optics and Precision Engineering 31(17):2573-2583(2023) DOI: 10.37188/OPE.20233117.2573.
针对舞台场景拍摄亮度动态范围受限问题,提出一种基于改进Retinex算法的舞台低照度增强方法。先对舞台场景的低照度图像采用改进的Retinex算法进行增强处理,得到整体增强后图像。将原图与增强后图像进行融合,处理掉过度增强和不需要增强的背景区域,得到最终图像。其中,改进的Retinex算法采用高斯-拉普拉斯高通滤波先求出反射分量后求出光照分量,解决反射分量细节丢失的问题。然后对反射分量进行对比度和细节增强处理,再与光照分量相乘得出增强后图像。本方法在软件平台验证的基础上进行FPGA硬件平台验证。实验结果表明,与其他经典方法相比,本方法在不同的舞台场景,尤其是明暗差距较大的舞台场景上,视觉效果明显,峰值信噪比(PSNR)平均提高了57.06%,结构相似性(SSIM)平均提高了27.34%。处理后的图像还原了舞台的真实亮度动态范围,并且色彩饱和度较好自然不失真,保证了较好的图像质量。
To address the problem of limited luminance dynamic range in stage scenes, this study proposes a method for low-light enhancement of the stage based on an improved Retinex algorithm. First, the low-light image of the stage scene is enhanced using the improved Retinex algorithm to obtain an overall enhanced image. Then, the original image is fused with the enhanced image, and background areas that are over-enhanced and do not need to be enhanced are processed to obtain the final image. The improved Retinex algorithm uses a Gauss-Laplace high-pass filter to find the reflected and illumination components, thus addressing the problem of detail loss in the reflection component. It then performs contrast and detail enhancement on the reflected component and multiplies it with the light component to produce the enhanced image. This method performs field-programable gate array (FPGA) hardware platform verification based on software platform verification. The experimental results show that, compared with other classical methods, this method yields a noticeable visual improvement, with an average increase of 57.06% in peak signal-to-noise ratio (PSNR) and 27.34% in structural similarity (SSIM) in different stage scenes. This improvement is particularly significant in stage scenes with substantial differences in brightness between light and dark areas. The images processed by this method restore the true luminance dynamic range of the stage and exhibit good natural color saturation without distortion, ensuring better image quality.
图像融合低照度增强Retinex算法舞台场景
image fusionlow light enhancementRetinex algorithmstage scenes
季渊,许怡晴,陈宝良,等. 硅基微显示器发展现状与研究进展 [J]. 激光与光电子学进展,2022,59 (20):92-103. doi: 10.3788/LOP202259.2011007http://dx.doi.org/10.3788/LOP202259.2011007
JI Y, XU Y Q, CHEN B L, et al.. Development and Research Progress of Silicon-Based Microdisplays [J]. Laser & Optoelectronics Progress, 2022, 59(20): 92-103. (in Chinese). doi: 10.3788/LOP202259.2011007http://dx.doi.org/10.3788/LOP202259.2011007
LAND E H, MCCANN J J. Lightness and retinex theory [J]. Journal of the Optical Society of America, 1971, 61(1):1-11. doi: 10.1364/josa.61.000001http://dx.doi.org/10.1364/josa.61.000001
JOBSON D J, RAHMAN Z, WOODELL G. Properties and performance of a center/surround retinex [J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 1997, 6(3): 451-462. doi: 10.1109/83.557356http://dx.doi.org/10.1109/83.557356
JOBSON D J, RAHMAN Z, WOODELL G. A multiscale retinex for bridging the gap between color images and the human observations of scenes [J]. IEEE Transcations on Image Processing,1997,7(6):965-976.
RAHMAN Z U, JOBSON D J, WOODELL G A. Retinex processing for automatic image enhancement[J].Journal of Electronic Imaging, 2004, 13(1):100-110. doi: 10.1117/1.1636183http://dx.doi.org/10.1117/1.1636183
WANG S H, ZHENG J, HU H M, et al. Naturalness preserved enhancement algorithm for non-uniform illumination images [J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2013, 22(9):3538-3548. doi: 10.1109/tip.2013.2261309http://dx.doi.org/10.1109/tip.2013.2261309
SHIN Y, JEONG S, LEE S. Adaptive brightness equalization based on retinex theory[C]. Proc of International Technical Conference on Circuits/Systems, Computers and Communications(ITC-CSCC2013), 2013:610-612.
陈刚,刘言,杨贺超,等. 低照度彩色图像的自适应亮度增强[J].光学 精密工程,2021,29(8):1999- 2007. doi: 10.37188/OPE.20212908.1999http://dx.doi.org/10.37188/OPE.20212908.1999
CHEN G, LIU Y, YANG H C, et al. Adaptive brightness correction of dim-lightening color images [J]. Opt. Precision Eng., 2021, 29(8):1999-2007. (in Chinese). doi: 10.37188/OPE.20212908.1999http://dx.doi.org/10.37188/OPE.20212908.1999
黄慧,董林鹭,刘小芳,等. 改进Retinex的低光照图像增强[J]. 光学 精密工程,2020,28(8):1835- 1849.
HUANG H, DONG L L, LIU X F, et al. Improved retinex low light image enhancement method [J]. Opt. Precision Eng., 2020,28(8):1835-1849. (in Chinese).
王殿伟,邢质斌,韩鹏飞,等. 基于模拟多曝光融合的低照度全景图像增强 [J]. 光学 精密工程, 2021, 29(2):349-362. doi: 10.37188/OPE.20212902.0349http://dx.doi.org/10.37188/OPE.20212902.0349
WANG D W, XING Z B, HAN P F, et al. Low illumination panoramic image enhancement algorithm based on simulated multi-exposure fusion [J]. Opt. Precision Eng., 2021, 29(2): 349-362. (in Chinese). doi: 10.37188/OPE.20212902.0349http://dx.doi.org/10.37188/OPE.20212902.0349
LORE K G, AKINTAYO A, SARKAR S. LLNet:A deep autoencoder approach to natural lowlight image enhancement [J].Pattern Recognition, 2017, 61:650-662. doi: 10.1016/j.patcog.2016.06.008http://dx.doi.org/10.1016/j.patcog.2016.06.008
WEI C H, WANG W J, YANG W H, et al. Deep retinex decomposition for low-light enhancement [J]. Arxiv Preprint Arxiv, 2018: 1808. 04560.
JIANG Y F, GONG X Y, LIU D, et al. EnlightenGAN: deep light enhancement without paired supervision [J]. IEEE Transactions on Image Processing, 2021, 30:2340-2349. doi: 10.1109/tip.2021.3051462http://dx.doi.org/10.1109/tip.2021.3051462
FU X, ZENG D, HUANG Y, et al. Color image detail enhancement based on quaternion guided filter [J].The Journal of China Universities of Posts and Telecommunications, 2017, 24(4):40-50. doi: 10.1016/s1005-8885(17)60222-xhttp://dx.doi.org/10.1016/s1005-8885(17)60222-x
刘寿鑫,龙伟,李炎炎,等. 基于HSV色彩空间的低照度图像增强 [J]. 计算机工程与设计, 2021, 42(9):2552-2560. doi: 10.16208/j.issn1000-7024.2021.09.020http://dx.doi.org/10.16208/j.issn1000-7024.2021.09.020
LIU S X, LONG W, LI Y Y, et al. Low-light image enhancement based on HSV color space[J]. Computer Engineering and Design, 2021, 42(9):2552-2560. (in Chinese). doi: 10.16208/j.issn1000-7024.2021.09.020http://dx.doi.org/10.16208/j.issn1000-7024.2021.09.020
CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698. doi: 10.1109/tpami.1986.4767851http://dx.doi.org/10.1109/tpami.1986.4767851
HUANG Z, FANG H, LI Q, et al. Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction[J]. Infrared Physics & Technology, 2018, 94:38-47. doi: 10.1016/j.infrared.2018.08.019http://dx.doi.org/10.1016/j.infrared.2018.08.019
CHANG Y, JUNG C, KE P, et al. Automatic contrast-limited adaptive histogram equalization with dual gamma correction [J]. IEEE Access, 2018, 6:11782-11792. doi: 10.1109/access.2018.2797872http://dx.doi.org/10.1109/access.2018.2797872
JEONG I, LEE C. An optimization-based approach to gamma correction parameter estimation for low-light image enhancement[J]. Multimedia Tools and Applications, 2021, 80(12):1-16. doi: 10.1007/s11042-021-10614-8http://dx.doi.org/10.1007/s11042-021-10614-8
YANG Y N, Improved retinex image enhancement al- gorithm based on bilateral filtering [C]. Proc of 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE 2015), 2015: 2250-2256. doi: 10.2991/icmmcce-15.2015.427http://dx.doi.org/10.2991/icmmcce-15.2015.427
ZUIDERVELD K. Contrast Limited Adaptive Histogram Equalization [M]. Graphics Gems, Amsterdam: Elsevier,1994:474-485. doi: 10.1016/b978-0-12-336156-1.50061-6http://dx.doi.org/10.1016/b978-0-12-336156-1.50061-6
MERTENS T, KAUTZ J, VAN REETH F V, Exposure Fusion : A simple and practical alternative to high dynamic range photography[J]. Computer Graphics Forum, 2009, 28(1):161-171. doi: 10.1111/j.1467-8659.2008.01171.xhttp://dx.doi.org/10.1111/j.1467-8659.2008.01171.x
周燕琴, 吕绪洋, 朱雄泳. 一种改进金字塔的多曝光HDR图像生成方法 [J]. 现代计算机, 2020, 15: 130-136. doi: 10.1007/springerreference_35099http://dx.doi.org/10.1007/springerreference_35099
ZHOU Y Q, LV X Y, ZHU X Y. An improved multi-exposure HDR image generation method by improved pyramids[J]. Modern Computers, 2020, 15:130-136. (in Chinese). doi: 10.1007/springerreference_35099http://dx.doi.org/10.1007/springerreference_35099
YING Z Q, LI G, GAO W, et al. A bio-inspired mul- ti-exposure fusion framework for low-light image enhancement[J]. Computer Vision and Pattern Recognition(cs.CV), 2017 ,14(8):1-10.
GUO X J, LI Y, LING H B. LIME: low-light image enhancement via illumination map estimation [J]. IEEE Transactions on Image Processing, 2017, 26(2):982-993. doi: 10.1109/tip.2016.2639450http://dx.doi.org/10.1109/tip.2016.2639450
REN Y R, YING Z Q, LI T H, et al. LECARM:low-light image enhancement using the camera response model[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(4):968-981. doi: 10.1109/tcsvt.2018.2828141http://dx.doi.org/10.1109/tcsvt.2018.2828141
0
Views
16
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution