UCT‐GAN: underwater image colour transfer generative adversarial network

Deng, Junjie; Luo, Guangchun; Zhao, Caidan

doi:10.1049/iet-ipr.2020.0003

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…The results show that this method is superior in enhancing the naturalness of images. Reference [19] generates a color projection image according to the designed nonlinear mapping function and the initial image, instructs the generative adversarial network to learn the inverse function of the nonlinear mapping function, and restores the color projection image. Reference [20] utilizes a progressive collaborative representation framework consisting of offline training and online optimization to remove mosaics from color images.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Jiao

2022

Journal of Sensors

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As the main carrier of information transmission, plane images play an important role in the current network era. In order to enhance the color of the plane image, optimize the image effect, and solve the problem of image distortion with large color difference, a color enhancement processing optimization method is proposed based on the technical support of computer vision. According to computer vision theory, a computer vision model for adjusting perceived color and brightness is constructed. Combining it with the bilateral filtering algorithm, a color enhancement processing optimization model is obtained, which consists of three main stages: illumination information parameter estimation and image color correction, reflection coefficient parameter estimation and image color correction, and adaptive filtering. By estimating illumination information parameters and reflection coefficient parameters, the secondary gamma correction of image color is realized. After being processed by the bilateral filtering algorithm, the final optimized image is obtained. Verified by the subjective and objective evaluation results, the positive correlation index values of this method are all high, 8.0246, 16.4526, 0.9037, and 15.0246, respectively, and the negative correlation index value is low, 49.4169. It is proved that the proposed method can effectively suppress the halo and artifacts and obtain the image color quality that satisfies the visual perception. It not only enhances the image color but also retains the details to the greatest extent.

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Section: Introductionmentioning

confidence: 99%

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Jiao

2022

Journal of Sensors

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“…Qualitative analysis revealed that the proposed method could significantly reduce the blue-green effect. In terms of a deep learning algorithm, to reduce the amount of data required while providing better image enhancement, Deng et al proposes an underwater image color transfer generative adversarial network (UCT-GAN) [36]. In [37], Chen et al proposed a new underwater image enhancement method based on deep learning and an image formation model.…”

Section: Color Enhancement Without the Information Of Light Attenuationmentioning

confidence: 99%

The Color Improvement of Underwater Images Based on Light Source and Detector

Quan

Wei

et al. 2022

Sensors

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As one of the most direct approaches to perceive the world, optical images can provide plenty of useful information for underwater applications. However, underwater images often present color deviation due to the light attenuation in the water, which reduces the efficiency and accuracy in underwater applications. To improve the color reproduction of underwater images, we proposed a method with adjusting the spectral component of the light source and the spectral response of the detector. Then, we built the experimental setup to study the color deviation of underwater images with different lamps and different cameras. The experimental results showed that, a) in terms of light source, the color deviation of an underwater image with warm light LED (Light Emitting Diode) (with the value of Δa*2+Δb*2 being 26.58) was the smallest compared with other lamps, b) in terms of detectors, the color deviation of images with the 3×CMOS RGB camera (a novel underwater camera with three CMOS sensors developed for suppressing the color deviation in our team) (with the value of Δa*2+Δb*2 being 25.25) was the smallest compared with other cameras. The experimental result (i.e., the result of color improvement between different lamps or between different cameras) verified our assumption that the underwater image color could be improved by adjusting the spectral component of the light source and the spectral response of the detector. Differing from the color improvement method with image processing, this color-improvement method was based on hardware, which had advantages, including more image information being retained and less-time being consumed.

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“…A premium recovery relies on a prodigious dataset where more data means more valuable features can be learned in latent space by the network. But concomitantly, datasets containing different scenes and characteristics often showcase various or even conflicting features, which may burden the model learning for domain‐specific tasks, e.g., underwater color correction, [ 18 ] high dynamic range imaging, [ 19 ] and vehicle compound lens imaging. [ 20 ] Although there has been a denoising dataset (SSID) [ 21 ] and a defocus blur dataset [ 13 ] dedicated to task‐specific imaging, they deviate from our goal in resolving clear images with both noise and defocus blur present due to temperature variance.…”

Section: Introductionmentioning

confidence: 99%

Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems

Chen

Liu

et al. 2022

Advanced Intelligent Systems

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Imaging systems are widely applied in harsh environments where the performance of shallow‐designed systems may deviate from expectation. As a representative scenario, environmental temperature variation may degrade image quality due to thermal defocus and sensor response, resulting in blur and noise. However, extensive athermalization in optics usually requires a complex design process and is limited by materials. Herein, a multibranch computational imaging scheme is developed, using emerging generative adversarial networks as the postprocessing to compensate for degradation of all kinds caused by thermal defocus and noise. In addition, a temperature controllable data acquisition, division, and mixture scheme is described to facilitate effective datasets for model robustness. Experiments on a vehicle lens and a mobile phone lens reveal that the proposed multibranch learned strategy notably increases image quality in the temperature range of 0–80 °C, and outperforms conventional athermalization in most instances, which is beneficial to lowering the design and manufacturing costs of imaging systems.

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UCT‐GAN: underwater image colour transfer generative adversarial network

Cited by 5 publications

References 29 publications

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

Optimization of Color Enhancement Processing for Plane Images Based on Computer Vision

The Color Improvement of Underwater Images Based on Light Source and Detector

Temperature‐Robust Learned Image Recovery for Shallow‐Designed Imaging Systems

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