Review on underwater image restoration and enhancement algorithms

Wang, Ruo‐Qian; Zhang, Jun; Fu, Xianping

doi:10.1145/2808492.2808548

Cited by 16 publications

(16 citation statements)

References 24 publications

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“…Although in the last years new methods have been proposed to compare and rank the quality of different algorithms (Osterloff et al, 2014;Panetta et al, 2015;Yang and Sowmya, 2015), the selection of the best algorithm can be challenging. Reviews of underwater image enhancement algorithms can be found in (Schettini and Corchs, 2010;Wang et al, 2015). A different but important category of image processing is computation of mosaics from still images (Gracias and Santos-Victor, 2000) so the observer can identify larger structures, covering a series of images, or to enable them to include large scale spatial context.…”

Section: Introductionmentioning

confidence: 99%

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

2016

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Marine imaging is transforming into a sensor technology applied for high throughput sampling. In the context of habitat mapping, imaging establishes thereby an important bridge technology regarding the spatial resolution and information content between physical sampling gear (e.g., box corer, multi corer) on the one end and hydro-acoustic sensors on the other end of the spectrum of sampling methods. In contrast to other scientific imaging domains, such as digital pathology, there are no protocols and reports available that guide users (often referred to as observers) in the non-trivial process of assigning semantic categories to whole images, regions, or objects of interest (OOI), which is referred to as annotation. These protocols are crucial to facilitate image analysis as a robust scientific method. In this article we will review the past observations in manual Marine Image Annotations (MIA) and provide (a) a guideline for collecting manual annotations, (b) definitions for annotation quality, and (c) a statistical framework to analyze the performance of human expert annotations and to compare those to computational approaches.

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Section: Introductionmentioning

confidence: 99%

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

2016

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“…To enhance visual effect, we use CLAHE (contrast limited adaptive histogram equalization) algorithm [15] to the image. Fig.…”

Section: Underwater Image Processing Distance Measurement Resultsmentioning

confidence: 99%

Distance Measurement of Underwater Target Based on Stereo Vision

Lin

Tang

2017

2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

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An underwater object location method based on binocular stereo vision and artificial auxiliary interaction is proposed. Firstly, the calibration principle and basic model of binocular stereo vision are given, and the relationship between binocular disparity and depth is deduced in detail. Then, a special underwater camera (Nano SeaCam) is used, and we can calibrate the camera directly underwater. After that, the parameters of the camera are obtained to rectify the underwater images. Further, according to the characteristics of underwater ROV (Remote Operated Vehicle), we use the method of artificial interaction to select the corresponding pixels of left and right view images, and then use the distance measurement algorithm to calculate the target distance. The experimental results show that this method can achieve the proper effect of measuring distance underwater, and the accuracy can meet the practical requirements, which is feasible and efficient.

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“…1). This results in reduced contrast, weak colors, and a visibility range between 5 and 20 m [3] or even less if there are disturbances in the water [4]. Underwater image (UI) treatment must consider physical phenomena originating from the medium and the effects resulting from the presence of organic and inorganic matter.…”

Section: Introductionmentioning

confidence: 99%

“…The UI treatment techniques have become a tool for pre-processing used by computer vision systems that require the identification of objects in underwater environments [5]. These techniques can be classified into two categories: restoration and enhancement techniques [3]. The first defines a model that simulates the degradation of the scene to reconstruct it.…”

Section: Introductionmentioning

confidence: 99%

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Analyzing pre-processing filters sequences for underwater-image enhancement

Ceballos¹,

Bolaño²,

Sánchez-Torres³

2017

ces

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Enhancement of the quality of a digital image is desirable in several scenarios such as underwater-image analysis, where improving visibility is necessary to reduce alterations caused by unbalanced lighting and the presence of sediments, among others. Many algorithms have been proposed oriented towards treatment of specific factors, such as contrast, color fidelity, noise, and lighting. This work explores the literature techniques and establishes a filter sequence for enhancing underwater images based on a pre-established quality metric. The resulting sequence begins by balancing the lighting using homomorphic filtering, improve the contrast by the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm, and finally noise reduction and edge enhancement are done by using bilateral filtering. The results of the implementation suggest a qualitative improvement, in contrast, color, and sharpness of borders.

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Review on underwater image restoration and enhancement algorithms

Cited by 16 publications

References 24 publications

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

RecoMIA—Recommendations for Marine Image Annotation: Lessons Learned and Future Directions

Distance Measurement of Underwater Target Based on Stereo Vision

Analyzing pre-processing filters sequences for underwater-image enhancement

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