PERBANDINGAN METODE HOMOMORPHIC FILTERING DAN METODE CONTRAST STRECHING UNTUK PERBAIKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Adi Mora Lubis ◽  
Nelly Astuti Hasibuan ◽  
Imam Saputra
Keyword(s):  
Digital Image ◽  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through a specific software. In the water environment, the light factor greatly influences the results of the quality of the image obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. . uneven lighting and bluish tones. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of repairing digital images that experience interference in lighting. The lighting repair process uses homomorphic filtering and uses contrast striching and will compare the quality of both methods and test to prove the results of image quality between homomorphic filtering and contrast streching. Until later the results of both methods can be seen which is better. homomorphic filtering and contrast stretching can produce image improvements with pretty good performance.Keywords: Digital Image, Underwater Image, Homomorphic Filtering, Contrast Streching, Matlab R2010a

IMPLEMENTASI METODE RETINEX UNTUK MENINGKATKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Bainun Harahap
Keyword(s):  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Object Search ◽  
Image Improvement ◽  
Underwater Object

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through certain software devices. In the water environment, light factors greatly influence the results of image quality obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. Underwater imagery is widely used as an object in various activities such as underwater habitat mapping, underwater environment monitoring, underwater object search. Uneven lighting and colors that tend to be bluish and runny. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of improvement in digital images that experience interference in lighting and exposure to sunlight. The lighting repair process uses the retinex method and will compare the quality of the two methods later. Until later the results of both methods can be seen which is better. Retinex method can produce image improvement with high performance.Keywords: Digital Cintra, Underwater, Matlab Retinex Method

scholarly journals Hazy Underwater Image Enhancement Based on Contrast and Color Improvement Using Fusion Technique

2017 ◽  
Vol 22 (3) ◽  
pp. 31-38
Author(s):  
Ritu Singh ◽  
Mantosh Biswas
Keyword(s):  
Image Enhancement ◽  
Fusion Technique ◽  
White Balance ◽  
Enhancement Technique ◽  
Underwater Image ◽  
Absorption Of Light ◽  
Contrast Stretching ◽  
Degraded Images

Abstract Scattering and absorption of light in water leads to degradation of images captured under the water. This degradation includes diminished colors, low brightness and undistinguishable objects in the image. To improve the quality of such degraded images, we have proposed fusion based underwater image enhancement technique that focuses on improving of the contrast and color of underwater images using contrast stretching and Auto White Balance. Our proposed method is very simple and straightforward that contributes greatly in uplifting the visibility of underwater images.

Underwater Color Image Enhancement Using Combining Schemes

2014 ◽  
Vol 48 (3) ◽  
pp. 57-62 ◽  
Author(s):  
Xin Luan ◽  
Guojia Hou ◽  
Zhengyuan Sun ◽  
Yongfang Wang ◽  
Dalei Song ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Color Contrast ◽  
Blue Color ◽  
Wavelet Domain ◽  
Nonuniform Illumination ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

AbstractUnderwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.

FOURIER TRANSFORM METHODS IN IMAGE ANALYSIS OF COMPRESSION WOOD AT THE CELLULAR LEVEL

IAWA Journal ◽  
2004 ◽  
Vol 25 (3) ◽  
pp. 311-324 ◽  
Author(s):  
Mattias K. Moëll ◽  
Minoru Fujita
Keyword(s):  
Fourier Transform ◽  
Compression Wood ◽  
Image Data ◽  
Cellular Level ◽  
Normal Wood ◽  
Two Dimensional ◽  
One Dimensional ◽  
Two Dimensional Image ◽  
Microscopic Features

Compression wood affects the overall quality of construction timber and paper quality. We have investigated the microscopic features of lumen shape and tracheid shape for compression wood studies and detection in softwoods. In this paper, we describe a method for directly analyzing tracheid and lumen shape over an entire image. The method uses the Fast Fourier Transform (FFT) and reduces the two-dimensional image data to one-dimensional data, from which lumen and tracheid shape can be evaluated. We illustrate the method by comparison of compression wood images to normal wood images. The results of detecting severe compression wood were successful, while the detection of weak compression wood was not satisfactory.

Formulating biossay data of chemicals and environmental water

2000 ◽  
Vol 42 (3-4) ◽  
pp. 115-123 ◽  
Author(s):  
R. Shoji ◽  
A. Sakoda ◽  
Y. Sakai ◽  
M. Suzuki
Keyword(s):  
Water Quality ◽  
Low Density Lipoprotein ◽  
Water Environment ◽  
Density Lipoprotein ◽  
Environmental Water ◽  
Toxicity Data ◽  
Human Beings ◽  
Environmental Waters ◽  
Bodies Of Water

The quality of environmental waters such as rivers is often deteriorated by various kinds of trace and unidentified chemicals despite the recent development of sewage systems and wastewater treatment technologies. In addition to contamination by particular toxicants, complex toxicity due to multi-component chemicals could be much more serious. The environmental situation in bodies of water in Japan led us to apply bioassays for monitoring the water quality of environmental waters in order to express the direct and potential toxicity to human beings and ecosystems rather than determinating concentrations of particular chemicals. However, problems arose from the fact that bioassays for pharmaceutical purposes generally required complicated, time-consuming, expert procedures. Also, a methodology for feedback of the resultant toxicity data to water environment management has not been established yet. To this end, we developed a novel bioassay based on the low-density lipoprotein (LDL) uptake activity of human hepatoblastoma cells. The assay enabled us to directly detect the toxicity of environmental waters within 4 hours of exposure. This is a significantly quick and easy procedure as compared to that of conventional bioassays. The toxicity data for 255 selected chemicals and environmental waters obtained by this method were organized by a mathematical equation in order to make those data much more effectively and practically useful to the management of environmental waters. Our methodology represents a promising example of applying bioassays to monitor environmental water quality and generating potential solutions to the toxicity problems encountered.

scholarly journals Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1079
Author(s):  
Vladimir Kazakov ◽  
Mauro A. Enciso ◽  
Francisco Mendoza
Keyword(s):  
Gaussian Processes ◽  
Research Method ◽  
General Algorithm ◽  
Two Dimensional ◽  
Recovery Algorithm ◽  
Conditional Mean ◽  
Quality Of Recovery ◽  
Input And Output ◽  
Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

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