We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or scene structure. It builds on the blending of two images that are directly derived from a color-compensated and white-balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low frequency components of the reconstructed image, we also adapt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.

/pdf/color-balance-and-fusion-for-underwater-image-enhancement-4cjuexzr3u.pdf

Color Balance and Fusion for Underwater Image Enhancement

The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance.

/pdf/underwater-image-processing-state-of-the-art-of-restoration-5bkw44ctbb.pdf

Underwater image processing: state of the art of restoration and image enhancement methods

Underwater images often suffer from color shift and contrast degradation due to the absorption and scattering of light while traveling in water. In order to handle these issues, we present and solve two sub-problems to improve underwater image quality. First, we introduce an effective color correcting strategy based on piece-wise linear transformation to address the color distortion. Then we discuss a novel optimal contrast improvement method, which is efficient and can reduce artifacts, to address the low contrast. Since most operations are pixel-wise calculations, the proposed method is straightforward to implement and appropriate for real-time application. In addition, prior knowledge about imaging conditions is not required. Experiments show an improvement in the enhanced image of color, contrast, naturalness and object prominence.

Two-step approach for single underwater image enhancement

Deriving inherent optical properties from background color and underwater image enhancement

Images taken under water usually suffer from the problems of quality degradation, such as low contrast, blurring details, color deviations, non-uniform illumination, etc. As an important problem in image processing and computer vision, the restoration and enhancement of underwater image are necessary for numerous practical applications. Over the last few decades, underwater image restoration and enhancement have been attracting an increasing amount of research effort. However, a comprehensive and in-depth survey of related achievements and improvements is still missing, especially the survey of underwater image dataset which is a key issue in underwater image processing and intelligent application. In this exposition, we first summarize more than 120 studies about the latest progress in underwater image restoration and enhancement, including the techniques, datasets, available codes, and evaluation metrics. We analyze the contributions and limitations of existing methods to facilitate the comprehensive understanding of underwater image restoration and enhancement. Furthermore, we provide detailed objective evaluations and analysis of the representative methods on five types of underwater scenarios, which verifies the applicability of these methods in different underwater conditions. Finally, we discuss the potential challenges and open issues of underwater image restoration and enhancement and suggest possible research directions in the future.

/pdf/an-in-depth-survey-of-underwater-image-enhancement-and-2h5bh2h7bf.pdf

An In-Depth Survey of Underwater Image Enhancement and Restoration

In this paper, a supervised enhancement method dedicated to underwater images is presented. It is well-known that the water alters the light propagation.Underwater images often present low contrast and luminosity. The aim of the proposed method is to correct the effects of the light propagation in the water. This method is based on the physical properties of the light. The originality of this processing is to work without any a priori information about the scene or about the water composition. Experiments show that this method increases the contrast and the color dynamic of underwater images while preserving a good color rendering. In addition, the analysis of the experimental results, conducts a relation between optical parameters and colorimetric attributes

Underwater Images Enhancement by Light Propagation Model Reversion

In this paper, a method of specific colored area extraction into a color image is presented. Usual color segmentation or edge detection operators perform a global processing on the image. Thus, every area into an image is detected. The whole image is subdivided into several region by labeled pixels, or boundaries in case of an edge detection. Our purpose is to extract only specific areas into an image sharing a specific color attribute, hence sharing a common color based on a specific hue. The quaternionic geometrical transformation into RGB color space is used. A specific hue axis is defined. From this axis, a criterion defines a color subspace into an RGB color space using the quaternionic HSI interpretation of RGB vectors.

Hue-based quaternionic criterion for focused-color extraction

Complementary CGH is used to embed a digital compressed version of a raw image in the micro-pattern halftone structure of the image microform. Impact on image quality recovery is evaluated.

Computer Generated Holographic Dual Storage Concept for Long Term Data Archiving

Il est avere que l'eau perturbe la propagation de la lumiere. C'est pour cette raison que les images sous-marines presentent souvent un faible contraste et une faible luminosite. Cet article est consacre a la presentation d'une methode non supervisee d'amelioration des images sous-marines. La methode proposee a pour but de compenser l'influence de l'eau en inversant le modele de propagation de la lumiere sans aucune information a priori sur la composition du milieu, c'est a dire l'eau. Pour inverser le modele de propagation de la lumiere, deux criteres sont proposes pour estimer d'une part les parametres de concentration des particules presentent dans l'eau et d'autres part la distance entre la scene et le capteur (la camera). Dans un premier temps, les parametres de concentration sont estimes en utilisant un lien particulier entre les pa- rametres de concentration et les proprietes colorimetriques de l'image. Dans un second temps, la distance entre la scene et le capteur est estimee iterativement. Cette estimation utilise un critere base sur une mesure d'erreur quadratique moyenne entre deux estimations successives de l'image corrigee. Une fois ces parametres determines, la correction est completement non supervisee. Les experimentations montrent l'efficacite de la methode. Le contraste et la dyna- mique des couleurs des images sous-marines sont ameliores tout en preservant un bon rendu visuel des couleurs.

Correction non supervisée d'images sous-marines couleur par inversion du modèle de propagation de la lumière

In this paper, an underwater image enhancement method using quaternions is presented. This work aims to improve color rendition and contrast of the objects, as if the scene has been taken out of water. This method is based on light attenuation inversion after processing a color space contraction using quaternions. Applied to the white, the attenuation gives a hue vector caracterizing the water color. Using this reference axis, geometrical transformations into the color space are computed with quaternions. Pixels of water areas of processed images are moved to gray or colors with a low saturation whereas the objects remain fully colored. Thus, the contrast of the observed scene is significantly improved and the difference between the background and the rest of the image is increased, giving a first approach towards a pre-segmentation step.

Underwater image enhancement by attenuation inversionwith quaternions

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Frederic Petit

Author Tools

Chat about Author