Wavelet based perspective on variational enhancement technique for underwater imagery

Vasamsetti, Srikanth; Mittal, Neerja; Neelapu, Bala Chakravarthy; Sardana, Harish Kumar

doi:10.1016/j.oceaneng.2017.06.012

Cited by 53 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Amjad et al [25] introduced a wavelet-based fusion method by using low-pass and high-pass filters to preserve desired frequencies presented in hazy underwater images. Vasamsetti et al [26] presented a framework of a waveletbased perspective enhancement technique for underwater images. Most of the model-free approaches are fast but suffer from overenhancement, color distortion and shifting [27].…”

Section: A Model-free Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Han

Guan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Underwater image enhancement has drawn much attention due to the significance of underwater vision. Although considerable progress has been made in this field, a key problem remains unsolved: how can we extract and enhance minutiae while trying to remove the noise caused by scattering and attenuation? To address this limitation, we propose a new underwater image enhancement technique with a novel spiral generative adversarial framework, named Spiral-GAN, which can effectively recover real-world underwater images with more details, vivid colors and better contrast. For steady training and color correction, we include the pixel-wise losses that consist of a mean squared error and an angle error in our objective function. In addition, we design our generator with several deconv-conv blocks to preserve the details from the original distorted images. Furthermore, we present a spiral learning strategy for generalizing the enhancing model to effectively recover the real-world underwater images. Finally, we perform a number of qualitative and quantitative evaluations that suggest that our proposed approach can efficiently enhance the quality of underwater images, which can be further used for underwater object detection. INDEX TERMS Underwater image enhancement, Generative adversarial networks, Spiral training.

show abstract

Section: A Model-free Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Han

Guan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Many methods have been reviewed in this work for both outdoor and underwater image enhancement domains. The area of research is broad and includes: Contrast Limited Adaptive Histogram Equalization (CLAHE) [17], Gamma Correction, and Generalized Unsharp Masking (GUM) [18], elative global histogram stretching (RGHS) [19], homomorphic filter and an anisotropic filter [20], wavelet-based fusion [21], wavelet-based perspective enhancement technique [22], CNN-based underwater image enhancement method [23], UIE-net (Underwater Image Enhancement-net) [24], WaterGAN [25], adopted GANs [26], Wasserstein GAN [27] and others.…”

Section: Image Enhancementmentioning

confidence: 99%

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Kazerouni

Dooly

Toal

2020

JMSE

View full text Add to dashboard Cite

Feature extraction and matching is a key component in image stitching and a critical step in advancing image reconstructions, machine vision and robotic perception algorithms. This paper presents a fast and robust underwater image mosaicking system based on (2D)2PCA and A-KAZE key-points extraction and optimal seam-line methods. The system utilizes image enhancement as a preprocessing step to improve quality and allow for greater keyframe extraction and matching performance, leading to better quality mosaicking. The application focus of this paper is underwater imaging and it demonstrates the suitability of the developed system in advanced underwater reconstructions. The results show that the proposed method can address the problems of noise, mismatching and quality issues which are typically found in underwater image datasets. The results demonstrate the proposed method as scale-invariant and show improvements in terms of processing speed and system robustness over other methods found in the literature.

show abstract

“…In the early research of underwater image enhancement technology, some traditional image enhancement algorithms in the air are often directly applied to underwater image processing. The traditional image enhancement algorithms can be divided into spatial domain method (G. Hou et al, 2018) (Ancuti et al, 2017) and frequency domain method (Jian et al, 2017) (Vasamsetti, et al, 2017). The spatial domain method is to directly process the pixel points in the image, using the method of gray mapping, such as selecting the appropriate mapping transformation to increase the contrast of the image, improve the gray level of the image and so on.…”

Section: Related Workmentioning

confidence: 99%

Underwater Image Enhancement Based on Polarization Imaging

Shen

Zhao

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The need of high-quality underwater imaging is obviously required in many underwater applications. For example, underwater archaeology, underwater ecological research, underwater object detection and tracking. This paper presents a joint enhancing and denoising scheme for an image taken in underwater conditions. Conventional image enhancing methods may amplify the noise depending on the distance and density of the particles in the water. To suppress the noise and improve the enhancement performance, an imaging model is modified by adding the process of amplifying the noise in underwater conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for enhancing the image. The proposed iterative underwater image enhancing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and enhanced irradiance image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different conditions are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme conditions without amplifying the noise.

show abstract

Wavelet based perspective on variational enhancement technique for underwater imagery

Cited by 53 publications

References 19 publications

Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Underwater Image Enhancement Based on a Spiral Generative Adversarial Framework

Underwater Image Enhancement and Mosaicking System Based on A-KAZE Feature Matching

Underwater Image Enhancement Based on Polarization Imaging

Contact Info

Product

Resources

About