Influence of range-gated intensifiers on underwater imaging system SNR

Region-specialized underwater image restoration in inhomogeneous optical environments

Range-gated laser imaging technology as a way of underwater detection featuring long detecting distance has a very wide range of applications in underwater exploration, reconnaissance, rescue and other fields. Digital image enhancement algorithm has been proved to have a significant effect to further improve the image quality of underwater range-gated laser imaging system. Based on lab's previous research on underwater range-gated laser imaging technology, we realize the local non-linear enhancement algorithm on FPGA hardware platform, which obtained real-time underwater laser range-gated imaging enhancement. Experiments were carried out in 18m long pipes full of water to compare and analyze several enhancement algorithms together. The results showed that the local non-linear enhancement algorithm in the paper significantly improved image quality compared to the conventional methods.

Underwater image restoration and enhancement using a Hybrid algorithm to evaluate the undersea activities like underwater vehicles to carry optical imaging systems for recording. The captured images and videos frequently suffered from two displeasing problems: 1. Color distortion; 2. Poor visibility. Those factors are the most notorious threats in underwater imaging systems because the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent. Under these inferences, an effective single underwater image restoration, and enhancement framework-based Sea-thru algorithm has been proposed for image restoration, depth estimation, and transmission compensation to enhance the image. To address the consequences of scattering and absorption, a new restoration algorithm outperformed by the state-of-the-art method both qualitatively and quantitatively. A wide variety of underwater images with various scenarios were exploited to assess the restoration performance of the proposed algorithm. The proposed underwater image restoration technique is a promising result for undersea activities that required high-quality images. Sea-thru method estimates backscatter using the dark pixels and machine learning algorithms, to create exciting opportunities for future underwater image exploration and conservation

Underwater polarimetric imaging for visibility enhancement utilizing active unpolarized illumination

For the underwater degenerative image caused by obscure and noise, atmospheric turbulence model is utilized to construct the restoration model. By computing the underwater image quality function which is used to estimate the adaptive degradation model parameter, underwater image is restored through Wiener filters. The connection between image contrast and average gradients and model parameter is analyzed as well. The experiments show that the proposed method exerts powerful effort on restoration of the obscure underwater image caused by scattering. The proposed method has a better improvement on contrast and definition of underwater image than other common preprocessing methods and is especially suitable for further degradation model analysis and objective detection.

In order to improve underwater range-gated imagery quality, image restoration and super-resolution reconstruction techniques based on maximum a posteriori framework are combined with the point spread function (PSF) of the system. A model based on beam propagation considering time-dependent backscatter is established including the extreme attenuation properties of water and the diffraction limit of sensor for obtaining the PSF. The proposed method is applied for an underwater range-gated imaging system. Performance of the approach is compared with the traditional restoration and reconstruction algorithms. Objective image quality metrics are used to quantify the effectiveness of the restoration and reconstruction. Experimental results showed that the proposed method can effectively improve the resolution and quality of underwater range-gated imaging.

Underwater optical imaging is often degraded by light scattering in chaotic media, leading to speckle noise, reduced resolution, and loss of detail. This paper presents a high‐resolution underwater imaging system that combines a Ta 2 O 5 /Ag composite (TOAC) structured random laser with the Real‐ESRGAN deep learning model. The TOAC structure prolongs photon multiple scattering paths through the scattering enhancement, and enhances the quantum yield of the gain through the localized surface plasmon resonance, which significantly reduces the random laser threshold and improves the quality factor to 1225.69. Compared with traditional lasers and halogen light sources, the image illuminated by this random laser has higher resolution and clearer details. Furthermore, random laser underwater imaging enhanced by Real‐ESRGAN deep learning can suppress speckles, boost image brightness, and recover details. For a resolution test chart imaged in turbid water, the application of Real‐ESRGAN nearly doubles the signal‐to‐noise ratio. Moreover, the reconstructed images of paramecia achieve a structural similarity index of 0.8532 against the undisturbed reference, representing a 2.44‐fold enhancement over the unreconstructed images. This study provides a new platform for high‐resolution and speckle‐free imaging in complex underwater environments, showcasing the potential of biological imaging, underwater biological monitoring, Underwater imaging, burr‐like Ta 2 O 5 /Ag NPs, deep learning, random laserand marine environmental research.

A multi-angle image enhancement method based on underwater imaging was proposed to solve the problems of color bias, geometric distortion and low contrast of the observed object in the complex and changeable underwater environment. Firstly, the gray world algorithm is used to preprocess the underwater image. Then an image optimization denoising algorithm based on K-SVD dictionary learning is used to keep the structure and texture information of the original image. Finally, combining with the underwater optical imaging model, the blue-green channel enhancement algorithm is used to realize the underwater imaging defog processing, which makes the restored image closer to the reality. The results show that this method can solve the problems of noise interference, fuzzy underwater image and low contrast.

A real-time image processing method based on a Field Programming Gate Array(FPGA) was proposed for Time Delay Integral(TDI)CCD space mosaic camera to improve its imaging quality and visual effect. Firstly, the TDICCD imaging system was corrected in a non-uniformity mode by a pixel level, by which the fixed-pattern noise and dark current noise were eliminated. Then, an adaptive median filter based on the inline max-min values was proposed to eliminate the random noise and pulse noise. Furthermore, an image after denoising was enhanced in the TDICCD image. By extracting the low-frequency part and the high frequency part of the image, the detail adjustment and contrast enhancement of the image were implemented. Finally, a linear stretching method based on image line data divided was proposed to improve the dynamic range of the real-time imaging system. Moreover, a real-time image processing method for the TDICCD space mosaic camera based on FPGA was designed, and the synthesized resources, algorithm errors and reliability in the FPGA were analyzed. The experimental results indicate that the average SNR of the imaging system has been improved from 48 db to 52 db, and the non-uniformity decreased from 3.41% to 1.73% within a TDICCD. The image contrast is enhanced greatly and the dynamic range is prompted by 6% in the self define criterion.

In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in underwater image defogging, presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.

Visual imaging under water is characterized by low range of visibility and poor image quality. Light interacts with water and its inherent particles in the form of absorption and scattering, causing image degradations, which have to by considered by underwater imaging systems. This can be done by optimizing the image acquisition and image post processing with respect to the properties of water and scene. Therefore a physically based computer graphical model is presented which describes the occurring effects of blurring, loss of contrast, color shift and brightening through backscattering. This model can be used for both: efficient simulation of underwater images and model based image restoration. Furthermore, the presented model can be used to answer theoretical questions on visibility range and imaging performance.

Underwater target detecting is an important technique for the development of the ocean engineering and exploration also a significant task of the ocean detecting. It plays a substantial role not only for the civil economy but also for the national security. The formation of the super-resolution underwater image is significant topic in ocean detecting field. In order to enhance the visual quality of images obtained by underwater imaging systems, super resolution (SR) reconstruction is introduced, including single-frame and multi-frame SR algorithms. Real-world images often contain singularities such as edges and high-frequency textured regions. As a result, these methods suffer from various edge-related visual artifacts such as ringing, aliasing, jagging, and blurring we mainly focus on super-resolution from single low resolution input image. The goal of single image super-resolution is to estimate a high resolution image from a low resolution input. In this paper, we propose a new high-quality and efficient single-image upscaling technique.

The range-gated laser imaging technology can eliminate the backscattering noise and has a range of from 4 to 6 times that of a conventional camera with floodlights in the strongly scattering waters, which becomes a useful technique in oceanic research, deep-sea exploration, underwater remote control and robotic works. The characteristics of range-gated underwater laser imaging were analyzed in this paper, and the basic requirements for gated ICCD were presented. As a result, Gated Gen II+ Image Intensifier and progressive scanning CCD were assembled together to meet the requirements, which was called the Gate Intensified CCD camera. Combined with the small-sized programmable high voltage power supply, video image acquisition/control system and high power lamp-Pumped blue-green Nd:YAG pulse laser, the experimental system was developed. Experiments were carried out in pipes full of saturated salt water and large pools. When tested by USAF 1951 resolution target, the detecting range can extend to about 30 meters.

Underwater laser imaging is of great significance in underwater search and marine science, <i>etc</i>. However, traditional underwater laser imaging is often of poor quality with noises and blurs, moreover, the resolution of the image is also low. In order to obtain clear underwater images with high resolution and quality, here, we have designed a range gated imaging underwater imaging system and realized an image restoration approach. In this paper, based on the introduction to the imaging system and image restoration algorithm, the experiment is established by setting the imaging system under water in the lake to capture the underwater targets. With the proposed underwater image restoration approach, images of high quality could be retrieved which proves that the method is able to identify the target ~10 meters away underwater.

Dark images are acquired under low light level condition and they have some weakness about image contrast and distortion of color. However, the most studies about dark image tried not to recover colors of the image, but to improve the image contrast. For this reason, we propose the method combined with the tone mapping and MSRCR in order to recovering the original color. We consider the tone mapping to control brightness of the image and the MSRCR to recover the original color. As experimental images, we employed synthesized images, created from the Barnard’s data set. We also consider the Euclidean distance and the angular error between images as the evaluation factors. The performance of the proposed method was evaluated by two experiments. First, we carried out the performance comparison of our method and some well-known color constancy methods. Secondly, we analyzed the performance according to change parameters of MSRCR. The experimental results tell us that the proposed method is more suitable for recovering the color of the dark images than the other methods.