With the development of deep learning, impressive progress has been made in the field of image restoration. The existing methods mainly rely on CNN and Transformer to obtain multi-scale feature information. However, these methods rarely integrate frequency domain information effectively during feature extraction, limiting their performance in image restoration. Additionally, few have combined Mamba with the Fourier domain for image restoration, which limits Mamba’s ability to perceive global degradation in the frequency domain. Therefore, we propose a new image restoration model called FMambaIR, which utilizes the complementarity between frequency and Mamba for image restoration. The core of FMambaIR is the F-Mamba block, which combines Fourier transform and Mamba for global degradation perception modeling. Specifically, F-Mamba adopts a dual branch complementary structure, including spatial Mamba branches and Fourier frequency domain global modeling. Mamba models the long-range dependencies of the entire image features, and the frequency branch utilizes Fourier to extract global degraded features from the image. Finally, we use a forward feedback network to integrate local information, which is beneficial for improving the recovery details. We comprehensively evaluate FMambaIR on several image restoration tasks, including underwater image enhancement, remote sensing image dehazing, and low-light image enhancement. The experimental results demonstrate that FMambaIR not only achieves superior performance compared to state-of-the-art methods but also significantly reduces computational complexity. Our code is available at https://github.com/mickoluan/FMambaIR.

Underwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.

A newly designed autonomous instrument was evaluated for long-term ocean turbulence measurements in the South China Sea from October 19, 2013, to February 10, 2014 at 21°09.90′N, 117°42.03′E. A shear velocity time series was collected continuously for 115 days at a single depth of 250 m using two orthogonal shear probes (PNS06). The mechanical design of wings is developed for an observation platform to achieve characteristics of stability and flexibility of current direction tracking. The depth and pitch in comparison with ambient current speed are analyzed, and the results indicate that the platform has satisfied stability requirements for turbulence measurement. The comparison between the instrument heading and current direction shows that the instrument flexibly responds to variations of ambient currents. Throughout the observation period, 62% of the data sets is of significant correlation with correlation coefficients above 0.5, and 4% is of strong correlation. The shear velocity spectra correspond well to the theoretical Nasmyth spectrum, and the magnitude of dissipation rates estimated from shear is approximately Ο(10⁻⁸) W kg⁻¹. The moored instrument is functional as designed and provides a reliable observation platform for extended turbulent measurements.

An autonomous Moored Reciprocating Vertical Profiler (MRVP) has been developed and tested for measuring ocean turbulence. The MRVP is designed to combine the advantages of long-term moored measurements at specified depths with those of short-term ship-supported continuous profiling performed at high vertical resolution. The profiler is programmed to repeat vertical motions autonomously along the mooring cable based on a buoyancy-driven mechanism. A sea trial has been conducted in the South China Sea to evaluate the performance of the profiler. The shear probe data are unreliable when the flow past sensors is not sufficiently greater than an estimate of turbulent velocity. For 65% of the dataset, turbulence measurements are of high quality and the magnitude of dissipation rates is up to O(10⁻¹⁰) W kg⁻¹. To minimize the contamination induced by instrument vibration and improve the estimation of turbulent kinetic energy terms, an advanced cross-spectrum algorithm is implemented to the measured shear data. The corrected spectra agreed well with the empirical Nasmyth spectrum, and dissipation rates had averagely decreased a factor of 2 and 8 times lower than the raw spectra. The autonomous MRVP is proven to be a stable platform, and the novel upward measurement provides a new perspective for measuring long-term time series of turbulence mixing.