Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

Al-Hashim, Mohammad Abid; Al-Ameen, Zohair

doi:10.18280/ts.370505

Cited by 21 publications

(15 citation statements)

References 36 publications

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“…To evaluate the effectiveness of the proposed enhancement algorithm, we compare the proposed method with conventional image enhancement algorithms and state-of-the-art technologies, i.e., multi-scale retinex (MSR) [ 7 ], multi-scale retinex with color restoration (MSRCR) [ 8 ], retinex-based Multiphase algorithm (RBMP) [ 23 ], and adaptive image enhancement method (AIEM) [ 22 ]. Six representative images with uneven illumination (image #1–6) are selected from the MEF [ 24 ] and NPE [ 18 ] image sets and combined with four nighttime images actually taken as the experimental test images (image #7–10).…”

Section: Experiments and Discussionmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Guided Filtering Enhancement

Yan

Qin

Zhang

et al. 2022

Entropy

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Image stitching refers to stitching two or more images with overlapping areas through feature points matching to generate a panoramic image, which plays an important role in geological survey, military reconnaissance, and other fields. At present, the existing image stitching technologies mostly adopt images with good lighting conditions, but the lack of feature points in scenes with weak light such as morning or night will affect the image stitching effect, making it difficult to meet the needs of practical applications. When there exist concentrated areas of brightness such as lights and large dark areas in the nighttime image, it will further cause the loss of image details making the feature point matching unavailable. The obtained perspective transformation matrix cannot reflect the mapping relationship of the entire image, resulting in poor splicing effect, and it is difficult to meet the actual application requirements. Therefore, an adaptive image enhancement algorithm is proposed based on guided filtering to preprocess the nighttime image, and use the enhanced image for feature registration. The experimental results show that the image obtained by preprocessing the nighttime image with the proposed enhancement algorithm has better detail performance and color restoration, and greatly improves the image quality. By performing feature registration on the enhanced image, the number of matching logarithms of the image increases, so as to achieve high accuracy for images stitching.

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Section: Experiments and Discussionmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Guided Filtering Enhancement

Yan

Qin

Zhang

et al. 2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Additionally, the figure depicts respective image histograms for each method. Different algorithms are the low-light image enhancement (LIME) algorithm [42], the Retinex based multiphase (RBM) algorithm [43], the bio-inspired multi-exposure fusion (BIMEF) algorithm [44], and the Zohair algorithm [45]. Upon applying various enhancement methods, it is observed that the peaks of histogram shift to the right, indicating an improvement in image brightness.…”

Section: The Evaluation Of Image Enhancement Methodsmentioning

confidence: 99%

Robust distance measurement using illumination map estimation and MAHNet in underground coal mines

Zhang,

Li,

Liu

et al. 2024

Meas. Sci. Technol.

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An effective binocular stereo distance measurement method is proposed to address the challenges posed by the low brightness and weak texture of images captured in underground coal mines for machine vision method. This approach is based on illumination map estimation and the MobileNetV3 attention hourglass stereo matching network (MAHNet) model. First, a binocular stereo vision system is established in which infrared LEDs are uniformly distributed on both sides of the belt conveyor bracket as visual feature points. Second, the images are preprocessed using illumination map estimation, and the optimization of inhomogeneous brightness image enhancement is achieved through the adoption of adaptive Gamma correction. Third, the YOLOv5 target detection network and Gaussian fitting fusion algorithm are utilized for the detection of infrared LED feature points. Fourth, the MAHNet model is employed to generate the cost volume and perform disparity regression, resulting in the acquisition of accurate disparity images. Finally, triangulation is applied to determine the depth of feature points. The experimental results of distance measurement demonstrate that an average relative ranging accuracy of 1.52% within the range of 50.0 cm to 250.0 cm can be achieved by the optimized method, thereby validating the effectiveness of this binocular distance measurement method in underground coal mines.

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“…In order to verify the superiority of the proposed algorithm in processing nighttime images, Ammen [ 27 ], Dong [ 28 ], SRIE [ 29 ], NPE [ 22 ], MF [ 15 ], Jiep [ 30 ], Ying [ 31 ], LIME [ 32 ], AIEM [ 7 ], and RBMP [ 33 ] have been chosen as the comparison algorithms. Nighttime images in the dataset of literature [ 34 ], which have a lot of dark light regions and contain artificial light sources, are selected to evaluate the proposed algorithm.…”

Section: Methodsmentioning

confidence: 99%

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

Yan,

Qin,

Zhang

et al. 2023

Entropy

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Image stitching technology realizes alignment and fusion of a series of images with common pixel areas taken from different viewpoints of the same scene to produce a wide field of view panoramic image with natural structure. The night environment is one of the important scenes of human life, and the night image stitching technology has more urgent practical significance in the fields of security monitoring and intelligent driving at night. Due to the influence of artificial light sources at night, the brightness of the image is unevenly distributed and there are a large number of dark light areas, but often these dark light areas have rich structural information. The structural features hidden in the darkness are difficult to extract, resulting in ghosting and misalignment when stitching, which makes it difficult to meet the practical application requirements. Therefore, a nighttime image stitching method based on image decomposition enhancement is proposed to address the problem of insufficient line feature extraction in the stitching process of nighttime images. The proposed algorithm performs luminance enhancement on the structural layer, smoothes the nighttime image noise using a denoising algorithm on the texture layer, and finally complements the texture of the fused image by an edge enhancement algorithm. The experimental results show that the proposed algorithm improves the image quality in terms of information entropy, contrast, and noise suppression compared with other algorithms. Moreover, the proposed algorithm extracts the most line features from the processed nighttime images, which is more helpful for the stitching of nighttime images.

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Retinex-Based Multiphase Algorithm for Low-Light Image Enhancement

Cited by 21 publications

References 36 publications

Nighttime Image Stitching Method Based on Guided Filtering Enhancement

Nighttime Image Stitching Method Based on Guided Filtering Enhancement

Robust distance measurement using illumination map estimation and MAHNet in underground coal mines

Nighttime Image Stitching Method Based on Image Decomposition Enhancement

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