Elimination of Marine Snow effect from underwater image - An adaptive probabilistic approach

Banerjee, Soma; Sanyal, Gautam; Ghosh, Sumit; Ray, R.K.; Shome, Sankar Nath

doi:10.1109/sceecs.2014.6804438

Cited by 15 publications

(9 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we present the first benchmarking performance for MSR Tasks 1 and 2. The methods used for the benchmarking are 1) MF [19], 2) adaptive MF [5], and 3) U-Net [28]. The kernel size of MFs is set to 3 × 3 or 5 × 5 pixels.…”

Section: Msr Benchmarking Resultsmentioning

confidence: 99%

“…Specific to MSR, a modified version of a MF is proposed in [5,17]. This method applies the MF selectively if the target pixel has a higher intensity than the surrounding pixels.…”

Section: Related Studiesmentioning

confidence: 99%

“…However, the particles are unevenly distributed in the scene, and the degradation process does not have a typical statistical property. Therefore, standard methods for marine snow removal are still limited to simple median-filter-based approaches [5,13,21]. Moreover, as with standard underwater image restoration, we do not have a pair of ground-truth and degraded image pairs to calculate the objective image qualities and tune the parameters of the restoration algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Msr Benchmarking Resultsmentioning

confidence: 99%

“…Specific to MSR, a modified version of a MF is proposed in [5,17]. This method applies the MF selectively if the target pixel has a higher intensity than the surrounding pixels.…”

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Marine Snow Removal Benchmarking Dataset

Sato,

Ueda,

Tanaka

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Most methods pursue marine snow removal in the interest of improving object detection pipelines, and therefore detect snow in the entire image. A family of filter-based approaches for marine snow detection and removal can be traced back to the work of Banerjee et al [2]. It presents a basic approach which does snow removal using median filtering and implicit snow detection based on the luminance channel of a YCbCr (luminance, blue-difference, reddifference) image-representation.…”

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

“…In [4], a adaptive probabilistic approach is proposed by considering the probability of marine snow existence based on pixel intensities in local patches. In [5], in a supervised manner, the marine snow is detected based on pixel dissimilarity with its their neighbors -in patches.…”

Section: Introductionmentioning

confidence: 99%

Marine Snow Detection for Real Time Feature Detection

Cardaillac

Ludvigsen

2022

2022 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV)

View full text Add to dashboard Cite

Underwater images are often degraded due to backscatter, light attenuation and light artifacts. One important aspect of it is marine snow, which are particles of varying shape and size. Computer vision technologies can be strongly affected by them and may therefore provide incorrect and biased results. In robotic applications, there is limited computational power for online processing. A method for real time marine snow detection is proposed in this paper based on a multi-step process of spatial-temporal data. The RGB colored images are converted to the YCbCr color space before they are decomposed to isolate the high frequency information using a guided filter for a first selection of candidates. Convolution with an uniform kernel is then applied for further analysis of the candidates. The method is demonstrated in two use cases, underwater feature detection and image enhancement.

show abstract

Elimination of Marine Snow effect from underwater image - An adaptive probabilistic approach

Cited by 15 publications

References 3 publications

Marine Snow Removal Benchmarking Dataset

Marine Snow Removal Benchmarking Dataset

Detecting and Suppressing Marine Snow for Underwater Visual SLAM

Marine Snow Detection for Real Time Feature Detection

Contact Info

Product

Resources

About