Low Illumination Image Enhancement Research Articles

Low-illumination image enhancement algorithm based on multi-feature fusion

In order to improve the effect of image acquisition under the condition of low illumination, a low illumination image enhancement algorithm based on multi-feature fusion is adopted in this paper. In this algorithm, the illumination information is extracted by bilateral filtering, and the illumination parameters are processed by adaptive gamma correction and limited contrast histogram equalization algorithm. The final illumination parameters are obtained by the feature weighted fusion of the processed parameters and the original illumination parameters. The RGB image is obtained by combining the illumination and reflection parameters, and the enhancement of the image is realized. The experimental results show that the algorithm used in this paper can not only improve the brightness of the image, but also enhance the characteristic parameters of the image, remove the noise, and make the color information of the image fuller and richer

结合分数阶微分及Retinex的NSCT自适应低照度图像增强

结合分数阶微分及Retinex的NSCT自适应低照度图像增强

基于注意力机制和Retinex的低照度图像增强方法

基于注意力机制和Retinex的低照度图像增强方法

Low-illumination image enhancement using a conditional generative adversarial network

Low-illumination image enhancement using a conditional generative adversarial network

基因表达式编程优化的色调保持低照度图像增强

基因表达式编程优化的色调保持低照度图像增强

Nighttime low illumination image enhancement with single image using bright/dark channel prior

Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

Low Illumination Image Enhancement Algorithm Based on DT-CWT and Tone Mapping

Low Illumination Image Enhancement Algorithm Based on DT-CWT and Tone Mapping

A novel enhancement method for low illumination images based on microarray camera

It is difficult but important to get clear information from the low illumination images. In recent years the research of the low illumination image enhancement has become a hot topic in image processing and computer vision. The Retinex algorithm is one of the most popular methods in the field and uniform illumination is necessary to enhance low illumination image quality by using this algorithm. However, for the different areas of an image with contrast brightness differences, the illumination image is not smooth and causes halo artifacts so that it cannot retain the detail information of the original images. To solve the problem, we generalize the multi-scale Retinex algorithm and propose a new enhancement method for the low illumination images based on the microarray camera. The proposed method can well make up for the deficiency of imbalanced illumination and significantly inhibit the halo artifacts as well. Experimental results show that the proposed method can get better image enhancement effect compared to the multi-scale Retinex algorithm of a single image enhancement. Advantages of the method also include that it can significantly inhibit the halo artifacts and thus retain the details of the original images, it can improve the brightness and contrast of the image as well. The newly developed method in this paper has application potential to the images captured by pad and cell phone in the low illumination environment.

Research on Vehicle Detection Method Based on Video in Color Low Illumination Environment

In this paper, we study the method of video vehicle detection in color low illumination environment at night. At first, the low illumination image enhancement algorithm based on multi-scale Retinex used to process the original color image, and we make the open operation noise reduction processing after extracting moving object by frame difference method. Because of the extracted motion vehicle has tomography, this paper presents an effective solution to determine whether the moving target is the same vehicle. In this way we solve the problem of the faultage. The results of experiments show the proposed method in this paper has better performance. The method not only meets the requirement of real-time vehicle detection, but also solves the problem of detecting moving vehicles at night.

A photographic negative imaging inspired method for low illumination night-time image enhancement

Images captured in low illumination conditions usually suffer from a poor visibility which has an important affect on the performance of computer vision systems. Thereby low illumination image enhancement is critical for image-related applications. In this paper, we proposed a photographic negative imaging inspired method for enhancing the images taken under low illumination environments. It consists of three basic components. First, the input night-time image is reversed to obtain its corresponding negative image (which is comparable with the latent image of a photographic film). Second, a rectification on the negative image is performed (Which is comparable with the chemical development in the process of film processing) by using an image dehazing method. This operation is inspired by the observation that the negative image looks like a hazed image and thereby it can be enhanced using a dehazing method. Third, the rectified negative image is reversed to obtain the final enhanced image (Which is comparable with the operation of fixing in the later stage of film processing.). Experiments over a large quantity of low contrast night-time images show that the proposed method is effective for enhancing low illumination images. Compared with six state-of-the-art image enhancement methods, the proposed method is superior to them in both enhancing image quality and decreasing time cost.