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The terahertz range, as a type of electromagnetic wave with wavelengths between microwaves and the infrared band, has the characteristics of penetration, low energy and a stable absorption spectrum of specific substances, and is widely used in non-destructive testing, human security inspections, biological tissue diagnoses and military detection. In particular, terahertz wave 3D imaging technology can detect the internal information of the target of detection, and it has become the focus of current research. This study carried out research on 3D reconstruction and object detection algorithms based on terahertz images. In view of the problem that the MVS (Multi-ViewStereo) series of 3D reconstruction algorithms ignore the context information between the cost layers and have unsatisfactory reconstruction effects when used on complex regions, an improved MVSNet 3D reconstruction algorithm FCTMVSNet(Feature and Cost Transformer Depth Inference for Unstructured Multi-view Stereo) based on Transformer is proposed here. A structured object recognition algorithm was designed to provide theoretical support for subsequent terahertz image-based object detection algorithms.

Terahertz waves, positioned between microwaves and infrared in the electromagnetic spectrum, are distinguished by their exceptional penetration capabilities, minimal energy requirements, and consistent absorption profiles for specific substances. Their versatility in applications such as non-destructive evaluation, human security scans, and biological diagnostics has propelled them to the forefront of scientific inquiry. However, existing terahertz equipment poses limitations in terms of compromised resolution, diffraction-induced blurring, and degradation of clarity due to texture overlaps. Consequently, numerous multi-view 3D reconstruction algorithms struggle to produce high-quality results with terahertz imagery. To address these challenges—particularly the scarcity of terahertz datasets and texture conflation—we integrated X-ray images from the DTU dataset with our collected terahertz projections. By observing inconsistent texture projections in multi-view terahertz images resulting from angle shifts, we developed DETransMVSnet—a state-of-the-art multi-view 3D reconstruction approach based on the Multi-Scale Deep Equilibrium Layer (MDEQ) paradigm. Leveraging equilibrium layers within homography-projected feature maps enables us to extract masks that differentiate different layers within a scene. The Intra-Attention and Mask-Attention Blocks further refine feature selection by preserving relevant terahertz details while suppressing disruptive background elements. As evidence of its effectiveness, DETransMVSnet achieves comparable performance to conventional algorithms on the DTU dataset but notably outperforms them when applied to terahertz datasets by successfully reconstructing images where previous methods have failed.

Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested.

Due to the scattering of atmospheric particles, images captured under hazy conditions suffer from contrast attenuation and color distortion, which severely affect the performance of machine vision systems. Various types of methods have been developed to improve the clarity of images. However, these methods are typically challenging to apply in real-time systems. We present a fast, single-image dehazing method based on the atmospheric scattering theory and dark channel prior theory. The transmission map is approximately estimated using a fast average filter, the subsection mechanism is designed to avoid the high brightness of the sky region in the recovered image, the region projection method is adopted to obtain the atmospheric light, and image color compensation is implemented using the Weber-Fechner law. Our experimental results show that this algorithm can restore images to a clear and natural state and ensure the balance of quality and the speed of image restoration. Therefore, the algorithm can be used in real-time systems.