One scan shadow compensation and visual enhancement of color images

Albu, Felix; Vertan, Constantin; Florea, Corneliu; Drîmbarean, Alexandru

doi:10.1109/icip.2009.5414437

Cited by 31 publications

(26 citation statements)

References 7 publications

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“…Here, ℂ C 11 represents the high CE coefficient at the i, j coordinate of the GL image I i, j . ℂ C 22 represents the low-CE coefficient at the i, j coordinate of the GL image I i, j . ℂ C 12 and ℂ C 21 represent the CE coefficient at the i, j coordinate of the GL image I i, j with the intermediate level of illumination.…”

Section: Region-based Grouping and Cementioning

confidence: 99%

Contrast‐based image enhancement algorithm using grey‐scale and colour space

Nandal

Bhaskar²

2018

IET signal process.

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The method presented encodes red, green and blue (RGB) images. When RGB images are added, the result inherently becomes darker due to summation of grey-scale images. This image is enhanced by adjusting brightness and contrast to achieve a visually pleasing image. The method developed uses hue saturation value (HSV) and contrast enhancement (CE) to obtain enhanced color images. It can be used to combine imaging modality of a multimodal data set to represent multimodal data sets in color image without data loss. In HSV color space, grey scale and color information are in separate channels, so the combined images do not suffer from the same darkening contrast as the images combined in RGB color space suffer. The method proposed first involves computation of mapping function, then gradient-based function is calculated to map image gradients based on grey-level (GL) intensity and eigenvalue computation is used for CE. Therefore, the contrast of GL components of gradient of an image is enhanced by a mapping function. A better visual output is ensured using the proposed algorithm which is evaluated using performance metrics. When compared to adaptive histogram equalisation and contrast-based imaging algorithms, the proposed technique gives significant performance in contrast and color enhancement.

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Section: Region-based Grouping and Cementioning

confidence: 99%

Contrast‐based image enhancement algorithm using grey‐scale and colour space

Nandal

Bhaskar²

2018

IET signal process.

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“…By using adaptive gamma correlation this enhances the image globally whereas DCT is applied to the high-frequency component of the image to strengthen the minute details of the image. Albu et al [20] have proposed a new method one scan shadow compensation (OSSC) that enhances low light areas while preserving the colour and details of an image. Capra et al [21] gave a method dynamic range optimisation by local contrast (DROLC) which is based on the local exponential correction.…”

Section: Introductionmentioning

confidence: 99%

Image contrast enhancement with brightness preservation using an optimal gamma and logarithmic approach

Singh

Bhandari

2020

IET Image Processing

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“…A myriad of algorithms have been proposed to recover the visual quality of weather-degraded images to be as similar as possible to the original ones taken under clear weather conditions. Low-light image enhancement [1][2][3][4], rain removal [5][6][7][8], and image dehazing [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are cases in point. Haze removal ones, of all the algorithms developed for visibility restoration, have positive impacts on both photography and computer vision applications.…”

Section: Introductionmentioning

confidence: 99%

Improved Color Attenuation Prior for Single-Image Haze Removal

2019

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This paper proposes a single image haze removal algorithm that shows a marked improvement on the color attenuation prior-based method. Through a vast number of experiments on a wide variety of images, it is discovered that there are problems in the color attenuation prior, such as color distortion and background noise, which arise due to the fact that the priors do not hold true in all circumstances. Successful resolution of these problems using the proposed algorithm shows its superior performance to other state-of-the-art methods in terms of both subjective visual quality and quantitative metrics, on both synthetic and natural hazy image datasets. The proposed algorithm also is computationally friendly, due to the use of an efficient quad-decomposition algorithm for atmospheric light estimation and a simple modified hybrid median filter for depth map refinement.

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One scan shadow compensation and visual enhancement of color images

Cited by 31 publications

References 7 publications

Contrast‐based image enhancement algorithm using grey‐scale and colour space

Contrast‐based image enhancement algorithm using grey‐scale and colour space

Image contrast enhancement with brightness preservation using an optimal gamma and logarithmic approach

Improved Color Attenuation Prior for Single-Image Haze Removal

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