Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Zhang, Weidong; Zhuang, Pinghui; Sun, Hai-Han; Li, Guohou; Kwong, Sam; Li, Chongyi

doi:10.1109/tip.2022.3177129

Cited by 354 publications

(108 citation statements)

References 66 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…Diverse human activities, including fishing and diving, are conducted in the accessible region of the undersea. 15 The properties of the UIFM must therefore be understood for research in the range of underwater fields to be performed.…”

Section: Underwater Image Formation Modelsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

View full text Add to dashboard Cite

valuable resources that exist beneath Earth's surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.

show abstract

Section: Underwater Image Formation Modelsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…Zhuang et al [31] proposed the Bayesian retinex algorithm for underwater image enhancement by imposing multi-step gradient priors on reflectance and illumination layers. [32] developed an underwater image enhancement method, called MLLE, which used integral and square integral maps to adjust image contrast, and addressed the color cast problem according to the principle of minimum color loss and maximum attenuation map. Wang et al [33] proposed the adaptive attenuation-curve prior based on a non-local prior, which relies on the statistical distribution of pixel values and utilized the saturation constraints to adjust the transmission map of RGB channels.…”

Section: Current Underwater Image Enhancementmentioning

confidence: 99%

Underwater Images Enhancement by Revised Underwater Images Formation Model

Pei

Chen

2022

IEEE Access

View full text Add to dashboard Cite

“…However, it is difficult to capture unified patterns in snowy scenes due to their different patterns and transparency. Unlike other types of image noises [1]- [4], snow seriously obscures the background and thus is difficult to be removed. How to recover clean images from snowy scenes is still a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

Lin¹,

Jiang²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Snow removal aims to locate snow areas and recover clean images without repairing traces. Unlike the regularity and semitransparency of rain, snow with various patterns and degradations seriously occludes the background. As a result, the state-of-the-art snow removal methods usually retains a large parameter size. In this paper, we propose a lightweight but high-efficient snow removal network called Laplace Mask Query Transformer (LMQFormer). Firstly, we present a Laplace-VQVAE to generate a coarse mask as prior knowledge of snow. Instead of using the mask in dataset, we aim at reducing both the information entropy of snow and the computational cost of recovery. Secondly, we design a Mask Query Transformer (MQ-Former) to remove snow with the coarse mask, where we use two parallel encoders and a hybrid decoder to learn extensive snow features under lightweight requirements. Thirdly, we develop a Duplicated Mask Query Attention (DMQA) that converts the coarse mask into a specific number of queries, which constraint the attention areas of MQFormer with reduced parameters. Experimental results in popular datasets have demonstrated the efficiency of our proposed model, which achieves the state-of-theart snow removal quality with significantly reduced parameters and the lowest running time.

show abstract

Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Cited by 354 publications

References 66 publications

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Underwater Images Enhancement by Revised Underwater Images Formation Model

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal

Contact Info

Product

Resources

About