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Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of high noise and insufficient feature extraction of Retinex-Net in low-light image enhancement processing, this paper proposes a new network structure. First, the Retinex-Net network was used as the basic model to decompose the input image, and a residual shrinkage network was introduced in the convolutional layer to remove the noise generated during the decomposition process. Then, in order to preserve the details of the image and suppress noise while enhancing the brightness, the enhancement network was divided into three small sub-networks for processing respectively. Finally, the adjusted images were fused to obtain an enhanced image. Compared with the SIRE、LIME、GLADNet、Retinex-Net algorithm, experiments show that the peak signal-to-noise ratio of the images processed by the algorithm in this paper has an avevage increase of 3.48 dB, the mean square error has an avevage increase of 0.082 7, the structural similarity has an avevage increase of 0.146, and the lightness order error has an avevage increase of 271.6.

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the existing learning-based low-light image enhancement methods are training models with paired data. Although they have achieved good results, they lose their original advantage in the absence of paired training sets. Moreover, in practice, it is almost impossible to obtain two images with different brightness from the same perspective in the same scene, so the model cannot be completely trained with paired data. Therefore, in order to avoid using paired data and make the model domain more adaptive, based on the Generative Adversarial Network, an unsupervised self-regularized attention mapping method for low-light image enhancement is proposed, which is called SAMGAN. This method not only uses the illumination information of the original image as self-regular mapping and grayscale image to enhance it, but also uses the feature self-feature preserving loss to retain the features and content of the original image. Not only can it be trained in the absence of low/ normal-light image pairs, but it can also be well extended to various real-world test images. A large number of experiments have proved that the proposed method is superior to many current methods in terms of visual quality and subjective user study. [ABSTRACT FROM AUTHOR]

Due to the influence of low lightness,the images acquired at dim or backlight conditions tend to have poor visual quality.Retinex-based low-light enhancement models are effective in improving the scene lightness,but they are often limited in hand-ling the over-boosted image noise hidden in dark regions.To solve this issue,we propose a Retinex-based low-light enhancement model incorporating the low-rank matrix approximation.First,the input image is decomposed into an illumination layer I and a reflectance layer R according to the Retinex assumption.During this process,the image noise in R is suppressed via low-rank-based approximation.Then,aiming to preserve the image details in the bright regions and suppress the noise in the dark regions simultaneously,a post-fusion under the guidance of I is introduced.In experiments,qualitative and quantitative comparisons with other low-light enhancement models demonstrate the effectiveness of our method.

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wth the improvement of night scene shooting technology, low-light image enhancement has become a new hot spot in the field of computer vision. However, due to the lack of light, backlighting, focusing failure and other factors will lead to insufficient light intensity, resulting in excessively low brightness and contrast of the image. To better process lowlight images, a low-light image enhancement algorithm based on multi-branch structure and U-net is proposed. The image features with different levels which are extracted by deep residual network are used for cross-merging. The obtained images are enhanced by U-net with different depths and structures, and then the images enhanced by U-net are fused. The enhanced low illuminance image is obtained. A mass of experiments show that the use of the deep residual network and U-net can better extract features, and the effect of low-intensity image enhancement is better, which is largely superior to the existing technology. The proposed methods not only improve the brightness and contrast visually, the colors are more realistic, and more in line with the characteristics of the human visual system, but also PSNR, SSIM and other seven objective image quality indexes are optimal in several algorithms. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of texture information loss, color offset distortion and reconstruction performance degradation in case of super resolution of images in low illumination environment, this paper proposed a super-resolution method of low illumination image based on color restoration and edge preservation. Based on anchored neighborhood regression (ANR), the method introduced the illuminance enhancement function of color restoration and edge preservation to improve the significance of image content and edge texture. Meanwhile, it selected the weighted least squares (WLS) as the central surround function to suppress the degradation of high-frequency features. Moreover, for the Y-channel component of YCbCr color space, it used the edge preserving illuminance enhancement function to calculate reflection component to further enhance the edge texture feature. The experimental results show that the proposed method achieves better visual effect. Compared with other methods, the PSNR of this method is improved by 63. 15%, SSIM is improved by 46. 86% and the perceived quality (PI) is improved by 4.12%. [ABSTRACT FROM AUTHOR]