Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024&#x00D7;1024&#x00D7;3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html. 

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Retinex-inspired color correction and detail preserved fusion for underwater image enhancement

Underwater image enhancement method via multi-feature prior fusion

MCI-Net: Multi-scale context integrated network for liver CT image segmentation

Underwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area.

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An Overview of Underwater Vision Enhancement: From Traditional Methods to Recent Deep Learning

Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the past few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that efficiently and effectively integrate both RGB Color Space and HSV Color Space in one single CNN. To our best knowledge, this method is the first one to use HSV color space for underwater image enhancement based on deep learning. UIEC^2-Net is an end-to-end trainable network, consisting of three blocks as follow: a RGB pixel-level block implements fundamental operations such as denoising and removing color cast, a HSV global-adjust block for globally adjusting underwater image luminance, color and saturation by adopting a novel neural curve layer, and an attention map block for combining the advantages of RGB and HSV block output images by distributing weight to each pixel. Experimental results on synthetic and real-world underwater images show that the proposed method has good performance in both subjective comparisons and objective metrics. The code is available at https://github.com/BIGWangYuDong/UWEnhancement .

UIEC^2-Net: CNN-based underwater image enhancement using two color space

An underwater image often suffers from quality degradation issues, such as color deviations, low contrast, and blurred details, due to the absorption and scattering of light. In this article, we propose to address the aforementioned degradation issues via attenuated color channel correction and detail preserved contrast enhancement. Concretely, we first propose an underwater image color correction method. Considering the differences between superior and inferior color channels of an underwater image, the inferior color channels are compensated via especially designed attenuation matrices. We then employ a dual-histogram-based iterative threshold method and a limited histogram method with Rayleigh distribution to improve the global and local contrast of the color-corrected image, thus achieving a global contrast-enhanced version and a local contrast-enhanced version, respectively. To integrate the complementary merits between the global contrast-enhanced version and the local contrast-enhanced version, we adopt a multiscale fusion strategy to fuse them. Finally, we propose a multiscale unsharp masking strategy to further sharpen the fused image for better visual quality. Extensive experiments on four underwater image enhancement benchmark data sets demonstrate that our method effectively enhances underwater images qualitatively and quantitatively. Besides, our method also generalizes well to the enhancement of low-light images and hazy images.

Underwater Image Enhancement by Attenuated Color Channel Correction and Detail Preserved Contrast Enhancement

In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection. To obtain a more efficient model architecture, we explore an architecture that has compatible capacities in the backbone and neck, constructed by a basic building block that consists of large-kernel depth-wise convolutions. We further introduce soft labels when calculating matching costs in the dynamic label assignment to improve accuracy. Together with better training techniques, the resulting object detector, named RTMDet, achieves 52.8% AP on COCO with 300+ FPS on an NVIDIA 3090 GPU, outperforming the current mainstream industrial detectors. RTMDet achieves the best parameter-accuracy trade-off with tiny/small/medium/large/extra-large model sizes for various application scenarios, and obtains new state-of-the-art performance on real-time instance segmentation and rotated object detection. We hope the experimental results can provide new insights into designing versatile real-time object detectors for many object recognition tasks. Code and models are released at https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet.

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN

Abstract. The demand to design lightweight semantic segmentation models on mobile devices is growing. Current U-shape structures can improve the segmentation accuracy. However, they can hardly achieve lightweight requirements due to their inefficient encoders. Besides, partial details and edges are damaged during the process of repeated downsampling. To this end, we propose an efficient and lightweight U-shape network (ELUNet) for real-time semantic segmentation. In the encoder, a light split-shuffle convolution block is designed as the key component of feature extraction to achieve high-precision segmentation in the resource-limited scene. Furthermore, we propose a bridge channel attention module in the skip connection to selectively emphasize the valuable features. In the decoder, we propose an upsample feature fusion module to capture global contextual information, significantly improving the ability of the network to extract spatial information. Moreover, we design an edge refinement module to refine the segmentation predictions further. Extensive experiments prove the effectiveness of the ELUNet on Cityscapes and Camvid benchmarks. Specifically, the ELUNet contains only 2.0M parameters and achieves 73.3% mIoU on Cityscapes validation set with the speed of 52.6 FPS for a 512  ×  1024 input image on a single 1080Ti GPU.

Yudong Wang

Papers

UIEC^2-Net: CNN-based underwater image enhancement using two color space

Underwater Image Enhancement by Attenuated Color Channel Correction and Detail Preserved Contrast Enhancement

RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Automatic bridge crack detection using Unmanned aerial vehicle and Faster R-CNN

ELUNet: an efficient and lightweight U-shape network for real-time semantic segmentation