Color corrected single scale retinex based haze removal and color correction for underwater images

Bindhu, A.; Maheswari, O. Uma

doi:10.1002/col.22568

Cited by 9 publications

References 38 publications

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Research on low-light image enhancement based on MER-Retinex algorithm

Zhou,

Wang,

Wang

et al. 2023

SIViP

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To solve blurring and poor visual effects after enhancement of low-light images by conventional low-light algorithms, this paper proposes a MER-Retinex (Multiscale Expansion Reconstruction Retinex) algorithm that integrates attention mechanism and multi-scale expansion pyramid reconstruction. It includes two parts: decomposition and enhancement module. In the decomposition module, two U-shaped networks are used to decompose the image into reflectance and illumination, then, use multi-layer convolution to expand the field of perception and improve the ability to decompose the image to obtain reflectance and illumination. In the enhancement module, a U-shaped network is used to fuse multi-scale expansion pyramids with a multi-attention mechanism to enrich image information, increase image brightness, and fuse the processed global information with local information to enhance the recovered image details. In the enhanced reconstruction section super-resolution techniques are used to enhance and denoise image feature details. Experimental analysis of the MER-Retinex algorithm was carried out on the LOL dataset. The PSNR of the algorithm in this paper was 25.26 and the NIQE was 3.43. The algorithm in this paper can effectively solve the problems of blurred images and poor visual effects, and has improved in both subjective perception and objective evaluation indexes.

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Research on low-light image enhancement based on MER-Retinex algorithm

Zhou,

Wang,

Wang

et al. 2023

SIViP

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A Hybrid Technique to Re-establish Squat Contrast Underwater Images Centred on Principal Component Analysis

Mittal

2023

SN COMPUT. SCI.

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Image haze removal based on rolling deep learning and Retinex theory

Huang

Liu

et al. 2021

IET Image Processing

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Multispectral remote sensing images are a very important data source, but its acquisition process is often affected by haze weather and other factors, resulting in the decline of image quality, blurred details and poor visual effect, which seriously affect their application and interpretation. To reduce the impact of haze on multispectral remote sensing image and improve the image clarity and the use value, a new haze removal method based on rolling deep learning and Retinex theory (RDLRT) was proposed here. It uses the rolling deep learning theory to realize the preliminary removal of image haze, and then calculates the peak signal-to-noise ratio (PSNR) value of the processed image, which is used to judge whether the obtained image is a stage effect image (SEI). Then, on the one hand, the SEI image is processed for colour saturation and brightness; on the other hand, the Retinex theory is used to further filter the haze of the SEI image. Finally, the results of the two processing are fused, and it is used as the restored image after removing the haze. A series of validation experiments were carried out with true multispectral remote sensing images, outdoor colour images and different methods, and good experimental results were obtained. The purpose of removing image haze and improving image quality is achieved. Through the comparison of these experiments, it is concluded that the RDLRT algorithm proposed in this paper can not only effectively remove haze in images, but also maintain good details and colour of the image, which has some popularization values for image pre-processing and restoration.

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Color corrected single scale retinex based haze removal and color correction for underwater images

Cited by 9 publications

References 38 publications

Research on low-light image enhancement based on MER-Retinex algorithm

Research on low-light image enhancement based on MER-Retinex algorithm

A Hybrid Technique to Re-establish Squat Contrast Underwater Images Centred on Principal Component Analysis

Image haze removal based on rolling deep learning and Retinex theory

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