WO2020107308A1 - Procédé et appareil d'amélioration rapide d'image à faible niveau de luminosité basés sur retinex - Google Patents

Procédé et appareil d'amélioration rapide d'image à faible niveau de luminosité basés sur retinex Download PDF

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Publication number
WO2020107308A1
WO2020107308A1 PCT/CN2018/118097 CN2018118097W WO2020107308A1 WO 2020107308 A1 WO2020107308 A1 WO 2020107308A1 CN 2018118097 W CN2018118097 W CN 2018118097W WO 2020107308 A1 WO2020107308 A1 WO 2020107308A1
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WO
WIPO (PCT)
Prior art keywords
retinex
image
low
pixel value
minimum pixel
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PCT/CN2018/118097
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English (en)
Chinese (zh)
Inventor
高峡
高月仁
李罡
杨海涛
康海凤
李海新
成景坤
耿华
史守帆
祁志雷
马桂泽
王鹏
谷明静
吕晓栓
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唐山曹妃甸联城科技有限公司
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Priority to PCT/CN2018/118097 priority Critical patent/WO2020107308A1/fr
Publication of WO2020107308A1 publication Critical patent/WO2020107308A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G06T5/94—Dynamic range modification of images or parts thereof based on local image properties, e.g. for local contrast enhancement

Definitions

  • the invention relates to the technical field of image processing, and in particular to a method and device for rapid enhancement of low-light images based on Retinex.
  • Image enhancement refers to the use of various mathematical methods and transformation methods to improve the contrast and clarity of objects of interest in an image to meet specific application image processing techniques.
  • Existing image enhancement technologies can be divided into two types: spatial unified methods and spatial non-unified methods.
  • the methods of spatial unification mainly include: logarithmic compression, gamma correction, histogram equalization, methods based on contrast sensitivity of human eyes, and methods based on Retinex. There are many methods in the latter category. They are often designed for specialized applications, so the algorithm works well, but the computational complexity is generally high. Among them, the most representative is the enhancement method based on Retinex.
  • the processing effect of Retinex on the input image is a non-linear process that imitates the human visual system.
  • the technical problem to be solved by the present invention is to provide a Retinex-based rapid enhancement method and device for low-light image to prevent the image from being too bright , Color cast phenomenon, and reduce the amount of calculation, improve processing efficiency.
  • a method for rapidly enhancing a low-light image based on Retinex includes:
  • the three channels R, G, and B of the original image are added to the difference corresponding to the difference map to obtain an enhanced color image.
  • the Retinex processing of the weighted Gaussian model on the minimum pixel value map includes:
  • calculating the weighted Gaussian model after weighting multiple Gaussian models is performed according to the following formula:
  • G represents the weighted Gaussian model
  • k 1...N
  • N represents the number of Gaussian templates
  • wk represents the weighting factor corresponding to the kth scale
  • Gk represents the kth Gaussian function.
  • the Retinex processing based on the weighted Gaussian model is performed on the minimum pixel value map according to the following formula:
  • I_retinex exp(logI_min-log(G*I_min))
  • I_Retinex represents the processed image
  • * represents the convolution operation
  • G is the weighted Gaussian model
  • I_min represents the minimum pixel map of pixels.
  • the above method further includes: performing adaptive brightness adjustment on the image processed by Retinex.
  • the adaptive brightness adjustment of the Retinex processed image includes:
  • a is the brightness adjustment parameter
  • Thred_high and Thred_low respectively represent the preset high brightness threshold and low brightness threshold
  • alow1 and alow2 respectively represent the brightness adjustment parameters of the corresponding conditions
  • I_Retinex represents the image after Retinex processing.
  • the brightness adjustment according to the brightness adjustment parameters specifically includes the following steps:
  • I_Retinex represents the image data after Retinex processing
  • a is the brightness correction parameter
  • I_corret represents the image data after brightness correction.
  • a device for rapidly enhancing a low-light image based on Retinex includes:
  • the minimum pixel value map acquisition module selects the minimum value of the R, G, and B channels of each pixel from the original image to obtain the minimum pixel value map;
  • the Retinex processing module performs Retinex processing of the weighted Gaussian model on the minimum pixel value map to obtain the Retinex processed image
  • the difference map calculation module calculates the difference map according to the Retinex processed image and the minimum pixel value map
  • the enhanced image calculation module adds each pixel R, G, and B of the original image to the difference corresponding to the difference map to obtain an enhanced color image.
  • the Retinex processing module further includes: a weighted Gaussian model calculation unit and a Retinex processing unit, wherein:
  • Weighted Gaussian model calculation unit used to calculate the weighted Gaussian model of multiple Gaussian models after weighting
  • the Retinex processing unit is used to perform Retinex processing based on the weighted Gaussian model on the minimum pixel value map.
  • the above device further includes an adaptive brightness adjustment module, which is used to perform adaptive brightness adjustment on the image processed by Retinex.
  • the adaptive brightness adjustment module further includes:
  • the adjustment parameter determination unit is used to determine the brightness adjustment parameter
  • the adjustment unit is used to adjust the brightness of the image processed by Retinex according to the adjustment parameters.
  • the method and device of the embodiment of the present invention can improve the contrast of the low-illumination area of the video image and highlight the details by using the weighted Gaussian model for Retinex processing, and at the same time change the original three convolution operations to only one convolution operation , Greatly reducing the amount of calculation and improving processing efficiency.
  • by performing adaptive brightness correction on the image processed by Retinex the phenomenon of over-brightness and partial color cast in the enhanced image is avoided.
  • FIG. 1 is a flowchart of a method for rapidly enhancing a low-light image based on Retinex according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method for rapidly enhancing low-light images based on Retinex according to a preferred embodiment of the present invention
  • FIG. 3 is a schematic structural diagram of a device for rapidly enhancing low-light images based on Retinex according to an embodiment of the present invention
  • FIG. 1 is a flowchart of a method for rapidly enhancing a low-light image based on Retinex according to an embodiment of the present invention. The method includes:
  • FIG. 2 is a flowchart of a Retinex-based low-light image rapid enhancement method provided by a preferred embodiment of the present invention. The method includes:
  • the color image processed by the present invention is R, G, B space, if it is other space image, it needs to be converted into R, G, B space first.
  • the minimum pixel value which is the minimum value of the three channels of a pixel, is defined as:
  • I_min min(R,G,B)(1)
  • R, G, B are the three color channels of the image
  • I_min is the minimum pixel value of the pixel.
  • I_retinex exp(logI_min-log(G*I_min)) (2)
  • I_Retinex represents the processed image data
  • * represents the convolution operation
  • I_min represents the minimum pixel map of pixels
  • G is the weighted Gaussian model:
  • G represents the weighted Gaussian model
  • k 1...N
  • N represents the number of Gaussian templates
  • wk represents the weighting factor corresponding to the kth scale
  • Gk represents the kth Gaussian function.
  • N takes 3
  • wk generally takes 0.3
  • its two-dimensional expression is:
  • ck is a scale constant, which determines the estimation of the incident component, that is, the final enhancement effect.
  • ⁇ k is a normalization factor such that:
  • a is the brightness adjustment parameter
  • Thred_high and Thred_low respectively represent the preset high brightness threshold and low brightness threshold
  • I_Retinex represents the image data after Retinex processing.
  • the R, G, and B channels of each pixel of the original input image are added to the corresponding Idiffer, respectively, to obtain an enhanced color image.
  • the weighted Gaussian model for Retinex processing by using the weighted Gaussian model for Retinex processing, it can improve the contrast of the low-illumination area of the video image, highlight the details, and change the original three convolution operations to only one convolution operation. Greatly reduce the amount of calculation and improve processing efficiency.
  • adaptive brightness correction is performed on the Retinex-processed image to avoid over-brightness and partial color cast in the enhanced image.
  • FIG. 3 it is a schematic structural diagram of a module for a rapid enhancement device for low-light images based on Retinex according to an embodiment of the present invention.
  • the device includes:
  • the minimum pixel value map acquisition module 10 is used to select the minimum value of the R, G, and B channels of each pixel from the original image to obtain a minimum pixel value map;
  • the Retinex processing module 20 is used to perform Retinex processing of the weighted Gaussian model on the minimum pixel value map to obtain the Retinex processed image;
  • the Retinex processing module 20 includes a weighted Gaussian model calculation unit 201 and a Retinex processing unit 202, where:
  • the weighted Gaussian model calculation unit 201 is used to calculate a weighted Gaussian model after weighting multiple Gaussian models
  • the Retinex processing unit 202 is used to perform Retinex processing based on the weighted Gaussian model on the minimum pixel value map.
  • Brightness adjustment module 30 used for adaptive brightness adjustment of the image processed by Retinex
  • the brightness adjustment module 30 includes an adjustment parameter determination unit 301 and an adjustment unit 302, where:
  • the adjustment parameter determination unit 301 is used to determine the brightness adjustment parameter
  • the adjusting unit 302 is used to adjust the brightness of the image processed by Retinex according to the adjustment parameters.
  • the difference map calculation module 40 calculates the difference map according to the Retinex processed image and the minimum pixel value map
  • the enhanced image calculation module 50 adds the difference value corresponding to the difference map to the three channels R, G, and B of each pixel of the original image to obtain an enhanced color image.
  • the contrast of the low-illuminance region of the video image can be improved, the details information can be highlighted, and the original three convolution operations are changed to only one convolution operation. Greatly reduce the amount of calculation and improve processing efficiency.
  • adaptive brightness correction is performed on the Retinex-processed image to avoid over-brightness and partial color cast in the enhanced image.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)

Abstract

L'invention concerne un procédé et un appareil d'amélioration rapide d'image à faible niveau de luminosité basés sur Retinex, appartenant au domaine technique du traitement d'images. Le procédé comprend : la sélection, à partir d'une image couleur d'origine, de la valeur minimale dans trois canaux de R, G et B de chaque point de pixel, de façon à obtenir une image de valeur de pixel minimale (S102); la réalisation d'un traitement Retinex d'un modèle gaussien pondéré sur l'image de valeur de pixel minimale pour obtenir une image ayant subi un traitement Retinex (S104); la réalisation d'un ajustement de luminosité auto-adaptatif sur l'image ayant subi un traitement Retinex, et le calcul d'une image de différence en fonction de l'image ayant subi un traitement Retinex et à luminosité ajustée et de l'image de valeur de pixel minimale (S106); et l'ajout de trois canaux de R, G et B respectivement de chaque point de pixel de l'image d'origine à des valeurs de différence correspondantes de l'image de différence, de façon à obtenir une image couleur améliorée (S108).
PCT/CN2018/118097 2018-11-29 2018-11-29 Procédé et appareil d'amélioration rapide d'image à faible niveau de luminosité basés sur retinex WO2020107308A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112255536A (zh) * 2020-09-21 2021-01-22 山东产研鲲云人工智能研究院有限公司 开关故障的检测方法、装置、电子设备及存储介质
CN115619659A (zh) * 2022-09-22 2023-01-17 北方夜视科技(南京)研究院有限公司 基于正则化高斯场模型的低照度图像增强方法与系统
CN117408906A (zh) * 2023-12-14 2024-01-16 中南大学 微光图像增强方法及系统

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CN102194220A (zh) * 2011-05-10 2011-09-21 西安理工大学 一种基于锐化强度与灰度分布结合的图像增强方法
CN103295206A (zh) * 2013-06-25 2013-09-11 安科智慧城市技术（中国）有限公司 一种基于Retinex的微光图像增强方法和装置
CN103295205A (zh) * 2013-06-25 2013-09-11 安科智慧城市技术（中国）有限公司 一种基于Retinex的微光图像快速增强方法和装置

Patent Citations (3)

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Publication number Priority date Publication date Assignee Title
CN102194220A (zh) * 2011-05-10 2011-09-21 西安理工大学 一种基于锐化强度与灰度分布结合的图像增强方法
CN103295206A (zh) * 2013-06-25 2013-09-11 安科智慧城市技术（中国）有限公司 一种基于Retinex的微光图像增强方法和装置
CN103295205A (zh) * 2013-06-25 2013-09-11 安科智慧城市技术（中国）有限公司 一种基于Retinex的微光图像快速增强方法和装置

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112255536A (zh) * 2020-09-21 2021-01-22 山东产研鲲云人工智能研究院有限公司 开关故障的检测方法、装置、电子设备及存储介质
CN112255536B (zh) * 2020-09-21 2023-05-26 山东产研鲲云人工智能研究院有限公司 开关故障的检测方法、装置、电子设备及存储介质
CN115619659A (zh) * 2022-09-22 2023-01-17 北方夜视科技(南京)研究院有限公司 基于正则化高斯场模型的低照度图像增强方法与系统
CN115619659B (zh) * 2022-09-22 2024-01-23 北方夜视科技(南京)研究院有限公司 基于正则化高斯场模型的低照度图像增强方法与系统
CN117408906A (zh) * 2023-12-14 2024-01-16 中南大学 微光图像增强方法及系统
CN117408906B (zh) * 2023-12-14 2024-03-19 中南大学 微光图像增强方法及系统

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