Journal ArticleDOI
Underwater image enhancement and restoration based on local fusion
TLDR
Experimental results show that the proposed method could effectively balance color distortion and enhance edges of the degraded images and is superior to many state-of-the-art methods.Abstract:
Underwater imaging and image processing play important roles in oceanic scientific research. However, because the light is absorbed and scattered, the obtained underwater images are seriously degraded. Color distortion, low contrast, and detail (edge information) loss are the major problems of underwater images. We propose a method to solve these problems. First, a local adaptive proportion fusion algorithm is proposed to produce a color-balanced image, which is the first input image. Second, an edge-enhanced image is produced as the second input image. Third, a proportion fusion image is produced as the third input image. Finally, the image formation model-based local triple fusion method is used to merge these three input images and get the final result. Experimental results show that the proposed method could effectively balance color distortion and enhance edges of the degraded images. Subjective and objective evaluations show that our method is superior to many state-of-the-art methods.read more
Citations
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Journal ArticleDOI
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
TL;DR: This work places multiple color charts in the scenes and calculated its 3D structure using stereo imaging to obtain ground truth, and contributes a dataset of 57 images taken in different locations that enables a rigorous quantitative evaluation of restoration algorithms on natural images for the first time.
Journal ArticleDOI
Underwater image restoration: A state‐of‐the‐art review
Proceedings ArticleDOI
Unveiling Optical Properties in Underwater Images
TL;DR: This work focuses on robust estimation of the water properties, and as opposed to previous methods that used fixed values for attenuation, estimates the veiling-light color from objects in the scene, contrary to looking at background pixels.
Book ChapterDOI
An Enhancement of Underwater Images Based on Contrast Restricted Adaptive Histogram Equalization for Image Enhancement
Vishal Goyal,Aasheesh Shukla +1 more
TL;DR: In this article, an adaptive histogram equalization (AHE)-based new underwater image enhancement technique is proposed to get enhanced results by adjusting the spacing between two adjacent gray levels adaptively to take target function as information entropy.
Journal ArticleDOI
An efficient single image haze removal algorithm for computer vision applications
TL;DR: An effective haze removal algorithm is reported for removing fog or haze from a single image and it is shown that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.
References
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Automatic underwater image pre-processing
TL;DR: The algorithm proposed in this paper is an automatic algorithm to pre-process underwater images which reduces underwater perturbations, and improves image quality and performance will be assessed using an edge detection robustness criterion.
Journal ArticleDOI
Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement
TL;DR: This paper presented a review of the detection and classification method of a foggy image, and summarized existing image defogging algorithms, including image restoration algorithms, image contrast enhancement algorithms, and fusion-based defogged algorithms.
Journal ArticleDOI
Fractional differential approach to detecting textural features of digital image and its fractional differential filter implementation
TL;DR: Experiments show that the fractional differential-based image operator has excellent feedback for enhancing the textural details of rich-grained digital images.
Journal ArticleDOI
Gamut Constrained Illuminant Estimation
TL;DR: A novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminants, the authors can recover an estimate of that light by means of a non-negative least-squares optimisation.