Phenomenological marine snow model for optical underwater image simulation: Applications to color restoration

Boffety, Matthieu; Galland, Frédéric

doi:10.1109/oceans-yeosu.2012.6263448

Cited by 15 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3(b) and (d), respectively. As clearly observed, they do not have a shape like a Gaussian function in contrast to the conventional assumption in [7,8]. Rather than a Gaussian function, these 3D plots are similar to elliptic conical frusta, i.e., sliced elliptic cones.…”

Section: Marine Snow Modelsmentioning

confidence: 72%

See 1 more Smart Citation

Marine Snow Removal Benchmarking Dataset

Sato,

Ueda,

Tanaka

2021

Preprint

View full text Add to dashboard Cite

This paper introduces a new benchmarking dataset for marine snow removal of underwater images. Marine snow is one of the main degradation sources of underwater images that are caused by small particles, e.g., organic matter and sand, between the underwater scene and photosensors. We mathematically model two typical types of marine snow from the observations of real underwater images. The modeled artifacts are synthesized with underwater images to construct large-scale pairs of ground-truth and degraded images to calculate objective qualities for marine snow removal and to train a deep neural network. We propose two marine snow removal tasks using the dataset and show the first benchmarking results of marine snow removal. The Marine Snow Removal Benchmarking Dataset is publicly available online.

show abstract

Section: Marine Snow Modelsmentioning

confidence: 72%

“…There have been few studies on modeling marine snow in digital images. The seminal study on modeling marine snow is described in [7,8]. A marine snow artifact is modeled using a Gaussian function in which the artifact is less transparent in its center than its surrounding area.…”

Section: Related Studiesmentioning

confidence: 99%

Marine Snow Removal Benchmarking Dataset

Sato,

Ueda,

Tanaka

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Marine snow mitigation for computer vision tasks is a relatively recent research topic. Early methods aimed at a more broad form of image enhancement modelled marine snow as a simple form of additive noise, however, more 1 https://www.ntnu.edu/arosvisiongroup/varos recent methods aimed specifically at marine snow point out the weaknesses of this approach, like its disregard of properties such as water absorption, size, shape, and backscattering [4].…”

Section: Related Workmentioning

confidence: 99%

Detecting and Suppressing Marine Snow for Underwater Visual SLAM

Hodne

Leikvoll

Yip

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

Conventional SLAM methods which work very well in typical above-water situations, are based on detecting keypoints that are tracked between images, from which egomotion and the 3D structure of the scene are estimated. However, in underwater environments with marine snow -small particles of organic matter which are carried by ocean currents throughout the water column -keypoint detectors are prone to detect the marine snow particles. As the vast majority of SLAM front ends are sensitive against outliers, and the marine snow acts as severe "motion noise", failure of the regular egomotion and 3D structure estimation is expected. For this reason, we investigate the structure and appearance of marine snow and developed two schemes which classify keypoints into "marine snow" or "clean" based on either the image patches obtained from usual keypoint detectors or the descriptors computed from these patches. This way the subsequent SLAM pipeline is protected against 'false' keypoints. We quantitatively evaluate the performance of our marine snow classifier on both real underwater video scenes as well as on simulated underwater footage that contains marine snow. These simulated image sequences have been created by extracting real marine snow elements from real underwater footage, and subsequently overlaying these on "clean" underwater videos. Qualitative evaluation is also done on a night-time road sequence with snowfall to demonstrate applicability in other areas of autonomy. We furthermore evaluate the performance and the effect of marine snow detection & suppression by integrating the snow suppression module in a full SLAM pipeline based on the pySLAM system.

show abstract

“…Marine snow refers to tiny particles that exist in the ocean and sink to the seabed. These speckles are composed of remnants of underwater organisms, floating fecal matter, suspended sediments, and other inorganic materials [ 5 ]. They exhibit various sizes, shapes, transparency and, as they settle to the seabed, resemble snowflakes, bearing similarities to atmospheric snow, hence the name marine snow.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Stage Progressive Network with Feature Transmission and Fusion for Marine Snow Removal

Liu,

Liao,

2024

Sensors

View full text Add to dashboard Cite

Improving underwater image quality is crucial for marine detection applications. However, in the marine environment, captured images are often affected by various degradation factors due to the complexity of underwater conditions. In addition to common color distortions, marine snow noise in underwater images is also a significant issue. The backscatter of artificial light on marine snow generates specks in images, thereby affecting image quality, scene perception, and subsequently impacting downstream tasks such as target detection and segmentation. Addressing the issues caused by marine snow noise, we have designed a new network structure. In this work, a novel skip-connection structure called a dual channel multi-scale feature transmitter (DCMFT) is implemented to reduce information loss during downsampling in the feature encoding and decoding section. Additionally, in the feature transfer process for each stage, iterative attentional feature fusion (iAFF) modules are inserted to fully utilize marine snow features extracted at different stages. Finally, to further optimize the network’s performance, we incorporate the multi-scale structural similarity index (MS-SSIM) into the loss function to ensure more effective convergence during training. Through experiments conducted on the Marine Snow Removal Benchmark (MSRB) dataset with an augmented sample size, our method has achieved significant results. The experimental results demonstrate that our approach excels in removing marine snow noise, with a peak signal-to-noise ratio reaching 38.9251 dB, significantly outperforming existing methods.

show abstract

Phenomenological marine snow model for optical underwater image simulation: Applications to color restoration

Cited by 15 publications

References 12 publications

Marine Snow Removal Benchmarking Dataset

Marine Snow Removal Benchmarking Dataset

Detecting and Suppressing Marine Snow for Underwater Visual SLAM

A Multi-Stage Progressive Network with Feature Transmission and Fusion for Marine Snow Removal

Contact Info

Product

Resources

About