Image cropping is a crucial means of enhancing the aesthetic quality of images. However, it relies on photographic knowledge and experience. For novice users, it is challenging to obtain satisfactory results through cropping. To meet the image cropping needs of novice users and improve efficiency, this article designs and develops an intelligent image cropping application, AesthetiCropper, and proposes two cropping methods. One is based on visual balance. The other is based on deep learning. This system features an intuitive user interface and automatically crops images to high aesthetic standards. It also offers customizable parameters and an editing module to meet users’ personalized requirements. User studies show that participants are more satisfied with the system’s cropping results compared to manual ones. The System Usability Scale (SUS) score indicates that its usability reaches a good level, making it worthy of promotion.

Image cropping is an important way to improve the composition of images, and it depends on aesthetic literacy and photography skills, which are the outcomes of years of training and practice. For novice users who lack photography knowledge, it’s difficult to get well-composed cropping results, and there is no application-level solution to help them. We develop an image cropping software MAGICROP, which is designed for novice users to make artful portrait images automatically. With an intuitive user interface, the software is enabled with both deep learning and composition rules for cropping operations. From the two functional models provided by the system, users can get inspiration of cropping and set customized parameters to generate the required cropping results. User studies verify the effectiveness of MAGICROP in creating quality images. The SUS score suggests acceptable usability and a promising space for continuous improvement.

The information extracted from waveform data of full-waveform light detection and ranging (LiDAR) has been widely used in applications such as 3D urban modeling, target recognition, and classification However, the presence of weak signals is inevitable in LiDAR systems. To enhance its effective detection capability and extraction accuracy, we propose a multispectral LiDAR (MSL) weak signals extraction (MSL-WSE) method. The measurement data from our MSL system were used to evaluate the performance of the proposed method. The correlation coefficient (R2), root mean square error (RMSE) and effective extraction rate show that the MSL-WSE method accurately detected and extracted the waveform parameters of weak echo signals, providing the more realistic and fine-grained true color 3D point cloud.

The full-waveform hyperspectral light detection and ranging (FWHSL) data have been widely used in surface topography, vegetation detection, and 3-D urban terrain modeling, capable of revealing the spatial distribution of a target and more detailed spectral information in the vertical direction. However, the echo signals of a target would significantly vary between different spectral channels due to the reflectance characteristics and the uneven energy distribution of supercontinuum laser source. Especially, band channels with weak reflectance over a long distance would affect the extraction accuracy of waveform parameters, which are essential for retrieving the spatial and spectral information of targets. This article proposes a multichannel interconnection decomposition method to improve the extraction accuracy of distance and spectral information at each pulse using hyperspectral waveform data. Two experiments were conducted to verify the performance of long-distance detection of targets using FWHSL. The first experiment detected a standard whiteboard, a green leaf, and a yellow leaf at roughly 518 m. Results demonstrated a considerable improvement in ranging precision and spectral detection using the proposed method compared with using the optimal channel with the best data quality. The second experiment simultaneously detected two adjacent targets at a distance of approximately 518 m. Results presented clear superiority of adding waveform channels in terms of discovering overlapping components and retrieving accurate waveform parameters. The success rate of extracting two targets 60 cm apart was greatly increased from 47% to 73% through the multichannel interconnection waveform decomposition (MIWD) method.