This paper proposes a new method for low-light image enhancement with balancing image brightness and preserving image details, this method can improve the brightness and contrast of low-light images while maintaining image details. Traditional histogram equalization methods often lead to excessive enhancement and loss of details, thereby resulting in an unclear and unnatural appearance. In this method, the image is processed bidirectionally. On the one hand, the image is processed by double histogram equalization with double automatic platform method based on improved cuckoo search (CS) algorithm, where the image histogram is segmented firstly, and the platform limit is selected according to the histogram statistics and improved CS technology. Then, the sub-histograms are clipped by two platforms and carried out the histogram equalization respectively. Finally, an image with balanced brightness and good contrast can be obtained. On the other hand, the main structure of the image is extracted based on the total variation model, and the image mask with all the texture details is made by removing the main structure of the image. Eventually, the final enhanced image is obtained by adding the mask with texture details to the image with balanced brightness and good contrast. Compared with the existing methods, the proposed algorithm significantly enhances the visual effect of the low-light images, based on human subjective evaluation and objective evaluation indices. Experimental results show that the proposed method in this paper is better than the existing methods.

High-precision segmentation of tobacco leaves is a prerequisite for analysis of phenotypic information. Challenges such as mutual occlusion and fuzzy edges make leaf segmentation difficult. This paper proposes an improved algorithm based on the Mask Region-based Convolutional Neural Networks (MASK RCNN) model and an instance segmentation method based on the SAM model to address these challenges. First, the MASK RCNN model is enhanced by incorporating a feature fusion layer and a hybrid attention mechanism, which improves the segmentation performance. The improved MASK RCNN model achieves an Avg.MIoU metric of approximately 85.10%, which is an improvement of 11.10% over the original algorithm. It also achieves an Avg.MPA metric of about 84.94%, indicating an improvement of 10.84%. Second, the Segment Anything Model (SAM) model is presented for the first time for tobacco leaf segmentation, providing empirical support for its application in the tobacco field. The SAM model demonstrates accurate segmentation of tobacco leaf images at different growth stages, demonstrating its good generality. In conclusion, the proposed methods effectively address the challenges in tobacco leaf segmentation, resulting in improved accuracy and performance. These techniques provide significant technical support for tobacco leaf phenotype research.

Lane detection is an important and challenging part of autonomous driver assistance systems and other advanced assistance systems. The presence of road potholes and obstacles, complex road environments (illumination, occlusion, etc.) are ubiquitous, will cause the blur of images, which is captured by the vision perception system in the lane detection task. To improve the lane detection accuracy of blurred images, a network (Lane-GAN) for lane line detection is proposed in the paper, which is robust to blurred images. First, real and complex blur kernels are simulated to construct a blurred image dataset, and the improved GAN network is used to reinforce the lane features of the blurred image, and finally the feature information is further enriched with a recurrent feature transfer aggregator. Extensive experimental results demonstrate that the proposed network can get robust detection results in complex environments, especially for blurred lane lines. Compared with the SOTA detector, the proposed detector achieves a larger gain. The proposed method can enhance the lane detail features of the blurred image, improving the detection accuracy of the blurred lane effectively, in the driver assistance system in high speed and complex road conditions.