Non-local means (NLM) are typically biased by the accumulation of small weights associated with dissimilar patches, especially at image edges. Hence, we propose to null the small weights with a soft threshold to reduce this accumulation. We call this method the NLM filter with a soft threshold (NLM-ST). Its Stein's unbiased risk estimate (SURE) approaches the true mean square error; thus, we can linearly aggregate multiple NLM-STs of Monte-Carlo-generated parameters by minimizing SURE to surpass the performance limit of single NLM-ST, which is referred to as the Monte-Carlo-based linear aggregation (MCLA). Finally, we employ a simple moving average filter to smooth the MCLA image sequence to further improve the denoising performance and stability. Experiments indicate that the NLM-ST outperforms the classic patchwise NLM and three other well-known recent variants in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and visual quality. Furthermore, its PSNR is higher than that of BM3D for certain images.

Most existing wake detection algorithms use Radon transform (RT) due to the long streak features of ship wakes in synthetic aperture radar images. The high false alarm rate of RT requires that the algorithms have significant human intervention for image preprocessing. When processing optical images, these algorithms are greatly challenged because it is not only sea clutter that interferes with the wake detection in the optical waveband but also many other environmental factors. Therefore, in this article, we address the automatic ship wake detection task in optical images from the idea of the convolutional neural network (CNN)-based object detection and design an end-to-end detector named WakeNet as a novel method. WakeNet processes all the wake textures clamped by the V-shaped Kelvin arms as the object and conducts detection via the oriented bounding box; the additional regression of wake tip coordinates and Kelvin arm direction can improve the hard wake detection performance while predicting the wake heading. According to the X-shaped wake spectral pattern and the wake edge enhancement principle in adjacent scales, spectral attention and multiscale attention modules are also used. Furthermore, more than 11.6k wake optical images are collected and annotated, providing a benchmark dataset. The experimental results on the dataset show that the proposed method has a better performance than other CNN-based methods, and the detection results of the yacht wake polarization images further verify its practical value. This article demonstrates that the deep learning-based method has considerable advantages over the traditional RT-based methods in wake detection.

Contrast enhancement regarded as a significant branch of image processing, not only enhances human perception of details hidden in the scene, but also improves the rapid recognition of interested targets. In this paper, a non-overlapped sub-blocks and local histogram projection based contrast enhancement (NOSHP) is presented. First, the original image is segmented into numbers of non-overlapped sub-blocks where the histogram projection (HP) is then executed individually. Subsequently, each sub-block is related to its adjacent three ones by certain weights, so that the integral image and local details can be both enhanced. Experiments in this paper approve that NOSHP can effectively enhance the local details as well as properly preserve the image brightness to avoid the annoying blocking effect and wash-out effect. Moreover, this algorithm can dramatically reduce the time consumption in practical use, leading to a useful real-time processing method well suited to the consumer electronic products.

The interaction between a moving submerged body and a homogeneous/stratified fluid generates a Bernoulli hump, a Kelvin wake, and an internal wake on the water surface. The height distribution of wakes is directly related to the location and motion state of the submerged body. Thus, it is possible to retrieve kinematics information of a submarine from the images of wakes obtained by photoelectric detection equipment. In order to extract the submarine’s location and velocity information effectively from the wake photoelectric images, we investigate the relationship between the motion state of moving submerged bodies and wakes in this paper. Bernoulli hump, Kelvin wake, and internal wake models are established based on potential flow theory and thin-ship approximation. We analyzed the wave crestline pattern and the energy characteristics of Kelvin wake components (divergent and transverse waves) and internal wakes at different velocities and diving depths. Finally, we propose a method for estimating the velocity of a submarine based on the wake wavelength and the diving depth inversion method based on the Fourier power spectrum of the Kelvin wake. The results obtained prove the feasibility of using photoelectric equipment to obtain wake images for use in analyzing the kinematics state of submarines, which are of guiding significance for detection and information processing in real-scale submarines.

Current methane gas leak detection technology uses infrared imaging in the medium wave (MW) or long wave (LW) bands, essentially applying cooled infrared detectors. In this study, a simplified three-layer radiative transfer model is adopted based on methane gas detection theory, considering background radiation, atmospheric infrared absorption, gas absorption, and emission characteristics to analyze the contrast of methane gas thermography in different infrared bands. The analysis results suggest that under certain conditions, the 6.6–8.6 μm LW band provides higher contrast compared to the 3–5 μm MW band. The optimal imaging wavelength band is selected according to imaging contrast advantages and disadvantages, and infrared optical systems and infrared filters are designed and optimized. We build a passive methane gas leak detection system based on uncooled infrared focal plane array detectors. By collecting gas images under different conditions, the imaging detection capabilities for methane gas leaks in the MW and LW bands in a laboratory environment are compared. Finally, the developing trends in methane gas detection technology are analyzed.

Unsupervised deep learning methods place increased emphasis on the process of cluster analysis of unknown samples without requiring sample labels.