Efficient L1-based nonlocal total variational model of Retinex for image restoration

Hou, Guiting; Pan, Huizhu; Huang, Baoxiang; Wang, Guodong; Wei, Weibo; Pan, Zhenkuan

doi:10.1117/1.jei.27.5.051207

Cited by 7 publications

(3 citation statements)

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“…Regarding color correction, the color constancy of underwater images means that when the color light irradiated on the surface of the object changes, people's cognition of the surface color of the object does not change with the change of the color of the irradiated light but maintains the original color cognition (Hou et al , 2018). The underwater scattering imaging model is introduced, which is described as the product of reflected image and incident image: …”

Section: Improved Relative Global Histogram Stretching Underwater Ima...mentioning

confidence: 99%

Research on small sample target detection for underwater robot

Luo,

Ruan,

2024

RIA

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Purpose The purpose of this paper is to propose a whole set of methods for underwater target detection, because most underwater objects have small samples, low quality underwater images problems such as detail loss, low contrast and color distortion, and verify the feasibility of the proposed methods through experiments. Design/methodology/approach The improved RGHS algorithm to enhance the original underwater target image is proposed, and then the YOLOv4 deep learning network for underwater small sample targets detection is improved based on the combination of traditional data expansion method and Mosaic algorithm, expanding the feature extraction capability with SPP (Spatial Pyramid Pooling) module after each feature extraction layer to extract richer feature information. Findings The experimental results, using the official dataset, reveal a 3.5% increase in average detection accuracy for three types of underwater biological targets compared to the traditional YOLOv4 algorithm. In underwater robot application testing, the proposed method achieves an impressive 94.73% average detection accuracy for the three types of underwater biological targets. Originality/value Underwater target detection is an important task for underwater robot application. However, most underwater targets have the characteristics of small samples, and the detection of small sample targets is a comprehensive problem because it is affected by the quality of underwater images. This paper provides a whole set of methods to solve the problems, which is of great significance to the application of underwater robot.

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Section: Improved Relative Global Histogram Stretching Underwater Ima...mentioning

confidence: 99%

Research on small sample target detection for underwater robot

Luo,

Ruan,

2024

RIA

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show abstract

“…The variational model is based on prior construction constraints. Hou et al [18] developed a Retinex algorithm under the variational framework, under the assumption that the illumination components vary smoothly in space, and calculated the illumination by gradient descent. Xie et al [19] proposed a method to estimate the probability of illumination and reflection at the same time, and demonstrated its good results on detail enhancement.…”

Section: Image Enhancementmentioning

confidence: 99%

An Image Sharpness Enhancement Algorithm Based on Green Function

Huang¹

2021

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The traditional image sharpness enhancement algorithm faces several defects, namely, the lack of details, and the poor subjective effect. To solve these defects, this paper proposes an image sharpness enhancement algorithm based on the Green function. Specifically, the Retinex model was employed to ensure that the enhanced image has outstanding details, and the Poisson’s equation was solved to maintain the consistency between the enhanced image and the original image in the gradient domain. Then, adaptive brightness mapping was carried out to determine the boundary conditions suitable for display, and the boundary of the region was sampled to reduce the complexity of our algorithm. Experimental results show that our algorithm improved the contrast and sharpness of images from the levels of contrastive image enhancement algorithms.

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“…Variational interpretations of Retinex are the set of enhancement algorithms [15,16]. The first variational framework was proposed by Kimmel et al [17] to estimate the illumination, and its objective function is established based on the smooth illumination assumption.…”

Section: Introductionmentioning

confidence: 99%

Variational Low-Light Image Enhancement Based on Fractional-Order Differential

Qianting Ma,

Yang Wang,

Tieyong Zeng

2024

CICP

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Images captured under insufficient light conditions often suffer from noticeable degradation of visibility, brightness and contrast. Existing methods pose limitations on enhancing low-visibility images, especially for diverse low-light conditions. In this paper, we first propose a new variational model for estimating the illumination map based on fractional-order differential. Once the illumination map is obtained, we directly inject the well-constructed illumination map into a general image restoration model, whose regularization terms can be viewed as an adaptive mapping. Since the regularization term in the restoration part can be arbitrary, one can model the regularization term by using different off-the-shelf denoisers and do not need to explicitly design various priors on the reflectance component. Because of flexibility of the model, the desired enhanced results can be solved efficiently by techniques like the plug-and-play inspired algorithm. Numerical experiments based on three public datasets demonstrate that our proposed method outperforms other competing methods, including deep learning approaches, under three commonly used metrics in terms of visual quality and image quality assessment.

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Efficient L1-based nonlocal total variational model of Retinex for image restoration

Cited by 7 publications

References 0 publications

Research on small sample target detection for underwater robot

Research on small sample target detection for underwater robot

An Image Sharpness Enhancement Algorithm Based on Green Function

Variational Low-Light Image Enhancement Based on Fractional-Order Differential

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