Since the light is absorbed and scattered while traveling in water, color distortion, under-exposure and fuzz are three major problems of underwater imaging. In this paper, a novel retinex-based enhancing approach is proposed to enhance single underwater image. The proposed approach has mainly three steps to solve the problems mentioned above. First, a simple but effective color correction strategy is adopted to address the color distortion. Second, a variational framework for retinex is proposed to decompose the reflectance and the illumination, which represent the detail and brightness respectively, from single underwater image. An effective alternating direction optimization strategy is adopted to solve the proposed model. Third, the reflectance and the illumination are enhanced by different strategies to address the under-exposure and fuzz problem. The final enhanced image is obtained by combining use the enhanced reflectance and illumination. The enhanced result is improved by color correction, lightens dark regions, naturalness preservation, and well enhanced edges and details. Moreover, the proposed approach is a general method that can enhance other kinds of degraded image, such as sandstorm image.

A retinex-based enhancing approach for single underwater image

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

A retinex-based enhancing approach for single underwater image(图像处理旗舰会议)

Underwater dam crack detection and classification based on visible images is a challenging task. The underwater environment is very complex with uneven illumination and serious noise problems, which often leads to the distortion of detection. In addition, there are few methods suitable for underwater dam crack classification. To solve these problems, a novel underwater dam crack detection and classification approach is proposed. Firstly, a dodging algorithm is used to eliminate the uneven illumination in the underwater visible images. Subsequently, a crack detection approach is proposed, where the local characteristics of image blocks and the global characteristics of connected domains are both used based on the analysis of the statistical properties of dam crack images. Finally, an improved evidence theory-based crack classification algorithm is proposed after the crack detection. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and...

A detection and classification approach for underwater dam cracks

The inspection of offshore parks, dam walls and other infrastructure under water is expensive and time consuming, because such constructions must be inspected manually by divers. Underwater buildings have to be examined visually to find small cracks, spallings or other deficiencies. Automation of underwater inspection depends on established water-proved imaging systems. Most underwater imaging systems are based on acoustic sensors (sonar). The disadvantage of such an acoustic system is the loss of the complete visual impression. All information embedded in texture and surface reflectance gets lost. Therefore acoustic sensors are mostly insufficient for these kind of visual inspection tasks. Imaging systems based on optical sensors feature an enormous potential for underwater applications. The bandwidth from visual imaging systems reach from inspection of underwater buildings via marine biological applications through to exploration of the seafloor. The reason for the lack of established optical systems for underwater inspection tasks lies in technical difficulties of underwater image acquisition and processing. Lightening, highly degraded images make a computational postprocessing absolutely essential.

Model based image restoration for underwater images

The arrangement has an inspection unit (1a) formed for optical detection of sample region (P), where the inspection unit comprises a microscope (19b). Another inspection unit (1b) is provided, with another microscope (19d), which is spatially spaced from the former inspection unit. A positioning unit (2) is provided with which the probe is transported by the former inspection unit in the optical detection region of the latter inspection unit after the optical detection of a sample region. The sample region is optically detected by the latter inspection unit. An independent claim is included for a method for optical detection of section sections, particularly sample region on a sample.

Arrangement for optical detection of sections, particularly sample region on sample, has inspection unit formed for optical detection of sample region, where inspection unit comprises microscope

When technical goods, like mainboards and other electronic components, are produced, quality assurance (QA) is very important. To achieve this goal, different optical microscopes can be used to analyze a variety of specimen to gain comprehensive information by combining the acquired sensor data. In many industrial processes, cameras are used to examine these technical goods. Those cameras can analyze complete boards at once and offer a high level of accuracy when used for completeness checks. When small defects, e.g. soldered points, need to be examined in detail, those wide area cameras are limited. Microscopes with large magnification need to be used to analyze those critical areas. But microscopes alone cannot fulfill this task within a limited time schedule, because microscopic analysis of complete motherboards of a certain size is time demanding. Microscopes are limited concerning their depth of field and depth of focus, which is why additional components like XY moving tables need to be used to examine the complete surface. Yet today’s industrial production quality standards require a 100 % control of the soldered components within a given time schedule. This level of quality, while keeping inspection time low, can only be achieved when combining multiple inspection devices in an optimized manner. This paper presents results and methods of combining industrial cameras with microscopy instrumenting a classificatory based approach intending to keep already deployed QA processes in place but extending them with the purpose of increasing the quality level of the produced technical goods while maintaining high throughput.

Integrating Microscopic Analysis into Existing Quality Assurance Processes

Anordnung zum Abtasten einer Probenflache (P) einer Probe mit einer zum Abtasten der Probenflache (P) ausgebildeten Inspektionseinheit (1), mit der die Probenflache (P) kennzeichnende Flachendaten (2) erzeugbar sind, einem Bildumsetzer (3), mit dem die die Probenflache (P) kennzeichnenden Flachendaten (2) in einen Bilddatensatz (4) umsetzbar sind, einer bevorzugt flachigen Anzeigeeinheit (5) zur visuellen Darstellung (6) des Bilddatensatzes (4) und einer Interaktionseinheit (7), mit der eine oder mehrere die Abtastung der Probenflache (P) kennzeichnende Abtasteinstellung(en) (8a bis 8f) der Inspektionseinheit (1) unter Heranziehen eines die visuelle Darstellung (6) des Bilddatensatzes (4) auf der Anzeigeeinheit (5) betrachtenden Benutzers (B) veranderbar ist/sind, wobei mit der Interaktionseinheit (7) ein Verhalten und/oder eine Reaktion des Benutzers (B) beim Betrachten der visuellen Darstellung (6) des Bilddatensatzes (4) auf der Anzeigeeinheit (5) erfassbar und auswertbar ist/sind und wobei mit der Interaktionseinheit (7) anhand des Auswertungsergebnisses die eine oder mehreren die Abtastung der Probenflache (P) kennzeichnende(n) Abtasteinstellung(en) (8a bis 8f) veranderbar ist/sind.

Anordnung zum Abtasten einer Probenfläche sowie entsprechendes Verfahren

Fraunhofer IOSB is currently constructing an automated microscopic laboratory. Different optical microscopes will be used to analyze specimen and gain new information out of the combination of the acquired sensor data. In order to narrow down specific patterns or tune the subsequent automated microscopy process, specimen have to be examined before the actual analysis process. To keep this step less time consuming and fatiguing, we designed an intuitive human-machine interface consisting of automated focusing algorithms and gaze-based interaction. The most important task in microscopy is focusing a desired region of a specimen. Considering microscopic analysis as a visual search task [1], this region of interest is exactly the region the operator is looking at. We use this information to automatically detect the focus plane in this region, by utilizing the microscope’s z-axis, and present the operator a focused image. The user does not need to know how to operate the microscope in detail, but however is successful by using the encapsulated sophisticated operational sequences like initial, continuous focusing or synthetic image enhancements. This paper presents results of realizations for guided and fully automated microscopic analysis incorporating intuitive interaction and eased operation while gaining high quality results.

Microscopic Analysis Using Gaze-Based Interaction

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.

Peter Frühberger

Author Tools

Chat about Author