SIDNet: A single image dedusting network with color cast correction

Huang, Jiayan; Xu, Haiping; Liu, Guanghai; Wang, Chuansheng; Hu, Zhongyi; Li, Zuoyong

doi:10.1016/j.sigpro.2022.108612

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…There are numerous image processing methods designed to improve image quality by the removal of different kinds of distortions [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is obvious that not every distortion can affect the entire image area; thus, every single pixel does not need to be processed.…”

Section: Introductionmentioning

confidence: 99%

“…In a direct way (Figure 3), all three images: distorted, corrected, and reference, are required, and the maps (TM/DM) are estimated in the process. The comparison of the distorted and reference images provides information about the pixels/channels that should be corrected (1). The comparison of distorted and corrected images provides knowledge of which pixels/channels are altered by the denoising algorithm (2).…”

mentioning

confidence: 99%

“…distorted and reference images provides information about the pixels/channels that should be corrected (1). The comparison of distorted and corrected images provides knowledge of which pixels/channels are altered by the denoising algorithm (2).…”

mentioning

confidence: 99%

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Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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“…Image restoration can be accomplished through the utilization of conventional color correction methods and deep learning-based approaches. In both of these techniques, a distorted image is commonly converted into the CIELAB color space, thereby dividing it into separate chromatic and lightness components [4]. The chromatic component undergoes color correction, while the lightness component is enhanced to generate a dust-free image that closely matches the original.…”

Section: Introductionmentioning

confidence: 99%

“…The chromatic component undergoes color correction, while the lightness component is enhanced to generate a dust-free image that closely matches the original. However, conventional image-dedusting techniques overlook the relationship between the depth of scene distortion and the depth of the dust-free image scene, focusing solely on image contrast [4]. Consequently, this approach often results in low color saturation and some remnants of dust in the final output image.…”

Section: Introductionmentioning

confidence: 99%

Improving Images in Turbid Water through Enhanced Color Correction and Particle Swarm-Intelligence Fusion (CCPF)

et al. 2023

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When light travels through a water medium, selective attenuation and scattering have a profound impact on the underwater image. These limitations reduce image quality and impede the ability to perform visual tasks. The suggested integrated color correction with intelligence fusion of particle swarm technique (CCPF) is designed with four phases. The first phase presents a novel way to make improvement on underwater color cast. Limit the improvement to only red color channel. In the second phase, an image is then neutralized from the original image by brightness reconstruction technique resulting in enhancing the image contrast. Next, the mean adjustment based on particle swarm intelligence is implemented to improve the image detail. With the swarm intelligence method, the mean values of inferior color channels are shifted to be close to the mean value of a good color channel. Lastly, a fusion between the brightness reconstructed histogram and modified mean particle swarm intelligence histogram is applied to balance the image color. Analysis of underwater images taken in different depths shows that the proposed CCPF method improves the quality of the output image in terms of neutralizing effectiveness and details accuracy, consequently, significantly outperforming the other state-of-the-art methods. The proposed CCPF approach produces highest average entropy value of 7.823 and average UIQM value of 6.287.

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Colour in Digitisation and Imaging

Jiang,

Westland

2024

Vision, Illusion and Perception

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SIDNet: A single image dedusting network with color cast correction

Cited by 14 publications

References 19 publications

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Improving Images in Turbid Water through Enhanced Color Correction and Particle Swarm-Intelligence Fusion (CCPF)

Colour in Digitisation and Imaging

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