UIPS: A Novel Image Resolution and Clarity Enhancement Scheme for Underwater Image Processing Scheme

Bapu, B. R. Tapas; Partheeban, N.; Nagaraju, V.; Kumar, N. Jagadish; Ali, Ahmed Mudassar

doi:10.1109/accai53970.2022.9752639

Cited by 6 publications

(1 citation statement)

References 8 publications

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“…It is not constrained by previous experience with the kinds and levels of underwater degraded images. Bapu et al 33 proposed a novel underwater image super-resolution technology known as the Underwater Image Processing Scheme (UIPS). A novel Perception-oriented SISR framework was proposed by Park et al 34 for perceptually accurate HR restoration.…”

Section: Sisrmentioning

confidence: 99%

Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Awad,

Zahan,

Lucas

et al. 2024

Pattern Recognition and Tracking XXXV

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Underwater imagery often exhibits significant degradation and poor quality as compared to outdoor imagery. To compensate for this, Single-Image Super-Resolution (SISR) and enhancement algorithms are used to lessen this degradation and produce high-resolution images. In this study, we apply state-of-the-art Simultaneous Enhancement and Super-Resolution (SESR) and SISR models to different sets of downscaled images from the comprehensive RUOD dataset. We then conduct a qualitative and quantitative analysis of the upscaled and enhanced images using standard underwater image quality metrics (IQMs). Subsequently, we evaluate the robustness of the state-of-the-art YOLO-NAS detector against image sets with varying downscaled spatial resolutions. Lastly, we examine the impact that the SISR and SESR models has on YOLO-NAS detector performance. The findings reveal a decline in the detection performance on the downscaled test images and a further decline on the upscaled and enhanced images produced by SISR and SESR models, suggesting a negative relationship between such models and detection.

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Section: Sisrmentioning

confidence: 99%

Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Awad,

Zahan,

Lucas

et al. 2024

Pattern Recognition and Tracking XXXV

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Diving into Clarity: Restoring Underwater Images using Deep Learning

Martinho,

Calvalcanti,

Pio

et al. 2024

J Intell Robot Syst

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In this paper we propose a learning-based restoration approach to learn the optimal parameters for enhancing the quality of different types of underwater images and apply a set of intensity transformation techniques to process raw underwater images. The methodology comprises two steps. Firstly, a Convolutional Neural Network (CNN) Regression model is employed to learn enhancing parameters for each underwater image type. Trained on a diverse dataset, the CNN captures complex relationships, enabling generalization to various underwater conditions. Secondly, we apply intensity transformation techniques to raw underwater images. These transformations collectively compensate for visual information loss due to underwater degradation, enhancing overall image quality. In order to evaluate the performance of our proposed approach, we conducted qualitative and quantitative experiments using well-known underwater image datasets (U45 and UIEB), and using the proposed challenging dataset composed by 276 underwater images from the Amazon region (AUID). The results demonstrate that our approach achieves an impressive accuracy rate in different underwater image datasets. For U45 and UIEB datasets, regarding PSNR and SSIM quality metrics, we achieved 26.967, 0.847, 27.299 and 0.793, respectively. Meanwhile, the best comparison techniques achieved 26.879, 0.831, 27.157 and 0.788, respectively.

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Image Resolution on Multiple Parameters using Spatial and Transform Domain: A Systematic Analysis

Joshi

Ajesh

Singh

et al. 2023

2023 International Conference on Disruptive Technologies (ICDT)

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UIPS: A Novel Image Resolution and Clarity Enhancement Scheme for Underwater Image Processing Scheme

Cited by 6 publications

References 8 publications

Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Underwater simultaneous enhancement and super-resolution impact evaluation on object detection

Diving into Clarity: Restoring Underwater Images using Deep Learning

Image Resolution on Multiple Parameters using Spatial and Transform Domain: A Systematic Analysis

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