Endoscopic image luminance enhancement based on the inverse square law for illuminance and retinex

Wang, Long; Wu, Baibo; Wang, Xiang; Zhu, Qingyi; Xu, Kai

doi:10.1002/rcs.2396

Cited by 15 publications

(23 citation statements)

References 36 publications

(66 reference statements)

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“…In this section, we compare the results of our proposed EIEN method with the results of six representative image enhancement methods, including MSRCR [ 9 ], AGCWD [ 6 ], LIME [ 11 ], Wang et al [ 12 ], Retinex-Net [ 14 ], Zero-DCE [ 16 ]. The performance of the proposed algorithm is evaluated from both subjective and objective aspects.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…The first row is the result of image enhancement, and the second row is the illumination component of the enhanced image, where the illuminance component is derived from our decomposition network. The figure shows that the enhancement results of other algorithms show over-enhancement or smoothing, while the image enhanced by EIEN has rich details [ 6 , 9 , 11 , 12 , 14 , 16 ].…”

Section: Figurementioning

confidence: 99%

“…The three sets of images ( a – c ) include photos of different parts of the human body. The classical image enhancement methods and the enhancement results of the method proposed in this paper are shown [ 6 , 9 , 11 , 12 , 14 , 16 ].…”

Section: Figurementioning

confidence: 99%

“…The classical image enhancement methods and the enhancement results of the method proposed in this paper are shown. The red box is the case where the local enhancement effect is not good [ 6 , 9 , 11 , 12 , 14 , 16 ].…”

Section: Figurementioning

confidence: 99%

“…Subsequently, Guo et al [ 11 ] proposed a structure-aware smoothing model to optimize the initial illumination component and obtain the enhanced image by elemental division. Wang et al [ 12 ] combined Retinex theory and the inverse square ratio law of illumination to propose an algorithm for endoscopic image enhancement with good results in illumination correction.…”

Section: Introductionmentioning

confidence: 99%

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EIEN: Endoscopic Image Enhancement Network Based on Retinex Theory

Chen

Qian

et al. 2022

Sensors

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In recent years, deep convolutional neural network (CNN)-based image enhancement has shown outstanding performance. However, due to the problems of uneven illumination and low contrast existing in endoscopic images, the implementation of medical endoscopic image enhancement using CNN is still an exploratory and challenging task. An endoscopic image enhancement network (EIEN) based on the Retinex theory is proposed in this paper to solve these problems. The structure consists of three parts: decomposition network, illumination correction network, and reflection component enhancement algorithm. First, the decomposition network model of pre-trained Retinex-Net is retrained on the endoscopic image dataset, and then the images are decomposed into illumination and reflection components by this decomposition network. Second, the illumination components are corrected by the proposed self-attention guided multi-scale pyramid structure. The pyramid structure is used to capture the multi-scale information of the image. The self-attention mechanism is based on the imaging nature of the endoscopic image, and the inverse image of the illumination component is fused with the features of the green and blue channels of the image to be enhanced to generate a weight map that reassigns weights to the spatial dimension of the feature map, to avoid the loss of details in the process of multi-scale feature fusion and image reconstruction by the network. The reflection component enhancement is achieved by sub-channel stretching and weighted fusion, which is used to enhance the vascular information and image contrast. Finally, the enhanced illumination and reflection components are multiplied to obtain the reconstructed image. We compare the results of the proposed method with six other methods on a test set. The experimental results show that EIEN enhances the brightness and contrast of endoscopic images and highlights vascular and tissue information. At the same time, the method in this paper obtained the best results in terms of visual perception and objective evaluation.

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