A New Database for Evaluating Underwater Image Processing Methods

Ma, Yupeng; Feng, Xiaoyi; Chao, Lujing; Huang, Dong; Xia, Zhaoqiang; Jiang, Xiaoyue

doi:10.1109/ipta.2018.8608131

Cited by 12 publications

(2 citation statements)

References 19 publications

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“…Further research could focus on improving high-quality reconstruction in these two aspects. If we have more datasets under water, we would apply the method in the study [47] to get reflectance and shading images under water.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network

Liu,

Ma,

Meng

et al. 2024

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Intrinsic Image Decomposition (IID) remains a pivotal challenge in the domain of computer vision, with applications spanning image editing, color image denoising, and segmentation, among others. Despite notable successes, there exists a significant opportunity for enhancing the feature encoding process to improve the accuracy of predicted outcomes. In response to this, a novel framework, termed Transformer and Laplacian Pyramid Network (TLPNet), is introduced. TLPNet comprises two distinct sub-networks: the Transformer for Reflectance Network (TRNet) and the Laplacian Pyramid for Shading Network (LPSNet). Within this framework, the Transformer module is strategically employed within the reflectance imaging component to effectively address the challenge of inadequate feature information extraction. Comprehensive experiments conducted on the ShapeNet Dataset and MIT Dataset have demonstrated the efficacy of TLPNet in predicting more accurate reflectance and shading images. This study contributes to the field by presenting an innovative approach that leverages the strengths of transformer models and Laplacian pyramid structures for the task of IID, setting a new benchmark for future research in the area.

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Section: Discussionmentioning

confidence: 99%

Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network

Liu,

Ma,

Meng

et al. 2024

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“…In 2018, Ma et al [15] presented a new underwater image database. It contains 6240 images from the 40 objects.…”

Section: Literature Workmentioning

confidence: 99%

A computational study and meta-analysis of content based image retrieval

Mathur¹,

Badone²

2019

IJATEE

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There is the need of efficient image retrieval in different areas including health industry and data processing. It is also important to fetch relevant data for the diagnosis, prediction and data correlation purpose. In the view of the above a study and analysis have been presented for the content based image retrieval (CBIR) methods and their approaches. The objective of this paper is to elaborate and explore the latest trend in this area for the purpose of discovering analytical and computational prospects. So different related and latest proposed and presented methods have been discussed along with the advantages and gaps. Based on the above, the limitations and the problem statements have been highlighted with the suggested solutions.

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A Systematic Approach to Synthesize Underwater Images Benchmark Dataset and Beyond

Liu

Chen

2019

2019 IEEE 15th International Conference on Control and Automation (ICCA)

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A New Database for Evaluating Underwater Image Processing Methods

Cited by 12 publications

References 19 publications

Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network

Enhancing Intrinsic Image Decomposition with Transformer and Laplacian Pyramid Network

A computational study and meta-analysis of content based image retrieval

A Systematic Approach to Synthesize Underwater Images Benchmark Dataset and Beyond

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