Multi-scale retinex improvement for nighttime image enhancement

Lin, Haoning; Shi, Zhenwei

doi:10.1016/j.ijleo.2014.07.118

Cited by 84 publications

(49 citation statements)

References 5 publications

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“…Ours restores more details than Lin's [11], such as the leaves at the top left corner and more textures of the road. However, Lin's [11] result reduces the glow of light at the side of the road, and has a more comfortable color than ours. …”

Section: Lightning Compensationmentioning

confidence: 95%

“…We also compare our method with MSR [5], Wang's [10] and Lin's [11] approaches for lightning compensation in Figures 13 and 14. Figure 13 shows an image with nonuniform illumination, which has a shadow region.…”

Section: Lightning Compensationmentioning

confidence: 99%

“…In this experiment, our approach is similar to Lin's. Both Lin's [11] and our approach compensate for the illumination and suppress the useless information in the dark region. Ours restores more details than Lin's [11], such as the leaves at the top left corner and more textures of the road.…”

Section: Lightning Compensationmentioning

confidence: 99%

“…Both Lin's [11] and our approach compensate for the illumination and suppress the useless information in the dark region. Ours restores more details than Lin's [11], such as the leaves at the top left corner and more textures of the road. However, Lin's [11] result reduces the glow of light at the side of the road, and has a more comfortable color than ours.…”

Section: Lightning Compensationmentioning

confidence: 99%

“…Therefore, Jobson continued his research and proposed the MSR (multiscale Retinex) algorithm [5], which has been the most widely used in recent years. Most improved Retinex algorithms [6][7][8][9][10][11][12][13][14][15] are based on MSR. However, the Gaussian filtering used by the MSR algorithm calculates a large number of floating data, which makes the algorithm take too much time.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

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Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model's design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.

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Section: Lightning Compensationmentioning

confidence: 95%

Section: Lightning Compensationmentioning

confidence: 99%

Section: Lightning Compensationmentioning

confidence: 99%

Section: Lightning Compensationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning

Han

2017

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Image enhancement using Retinex and image fusion techniques

Yamakawa

Sugita

2018

Elect Comm in Japan

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The image quality improvement techniques based on Retinex theorem are effective for enhancement of visibility in dark area, while visibility in bright area is degraded. This paper presents an image fusion technique using source image and Retinex-processed image in order to implement high visibility in both bright and dark areas. In the proposed method, the statistic that corresponds to the subject's evaluation is used for image fusion. It is shown through some experiments that the proposed method can generate the image with high visibility in both bright and dark areas, with less computational cost compared with a conventional image fusion method. K E Y W O R D Shigh visibility, image fusion, image quality improvement, rescaling SSR, Retinex 52

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