scholarly journals Deep Learning Based Filtering Algorithm for Noise Removal in Underwater Images

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2742
Author(s):  
Aswathy K. Cherian ◽  
Eswaran Poovammal ◽  
Ninan Sajeeth Philip ◽  
Kadiyala Ramana ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Noise Removal ◽  
Image Resolution ◽  
Similar Data ◽  
Underwater Imaging ◽  
Edge Information ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Water Sensing

Under-water sensing and image processing play major roles in oceanic scientific studies. One of the related challenges is that the absorption and scattering of light in underwater settings degrades the quality of the imaging. The major drawbacks of underwater imaging are color distortion, low contrast, and loss of detail (especially edge information). The paper proposes a method to address these issues by de-noising and increasing the resolution of the image using a model network trained on similar data. The network extracts frames from a video and filters them with a trigonometric–Gaussian filter to eliminate the noise in the image. It then applies contrast limited adaptive histogram equalization (CLAHE) to improvise the image contrast, and finally enhances the image resolution. Experimental results show that the proposed method could effectively produce enhanced images from degraded underwater images.

An Image Enhancement Method Based on the Coal Mine Monitoring System

2012 ◽  
Vol 468-471 ◽  
pp. 204-207
Author(s):  
Zhen Chong Wang ◽  
Yan Qin Zhao
Keyword(s):  
Image Enhancement ◽  
Coal Mine ◽  
Impulse Noise ◽  
Histogram Equalization ◽  
Video Monitor ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Special Environment

For the low illumination and low contrast in the coal mine, images captured from the video monitor system sometimes are not so clear to help the related personal monitoring the production and safety of the mine. According to the special environment of coal mine, an image enhancement method was presented. In this method the impulse noise which is the mainly noise in the coal mine was first reduced with median filtering, then the low contrast and illumination was greatly improved with the improved adaptive histogram equalization. Experiments show that this method can improve the quality of images underground effectively.

Assessment of Some Enhancement Methods of Renal X-ray Image

2020 ◽  
Vol 18 (12) ◽  
pp. 01-05
Author(s):  
Salim J. Attia
Keyword(s):  
Image Quality ◽  
Radiation Dosage ◽  
Histogram Equalization ◽  
Quality Factors ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Quality Image ◽  
Enhancement Method ◽  
Spatial Quality

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

scholarly journals An image enhancement method based on gabor filtering in wavelet domain and adaptive histogram equalization

2021 ◽  
Vol 21 (1) ◽  
pp. 146
Author(s):  
Jeevan K M ◽  
Anne Gowda A B ◽  
Padmaja Vijay Kumar
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Median Filtering ◽  
Wavelet Domain ◽  
Gabor Filtering ◽  
Low Contrast ◽  
Enhancement Technique ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Two Parameters

<p><span>The images are not always good enough to convey the proper information. The image may be very bright or very dark sometime or it may be low contrast or high contrast. Because of these reasons image enhancement plays important role in digital image processing. In this paper we proposed an image enhancement technique in which Gabor and median filtering is performed in wavelet domain and Adaptive Histogram Equalization is performed in spatial domain. Brightness and contrast are the two parameters used for analyzing the performance of the proposed method</span></p>

scholarly journals Dynamic range reduction of infrared images based on adaptive equalization, stretch and compression of histogram

2021 ◽  
Vol 66 (4) ◽  
pp. 470-482
Author(s):  
S. I. Rudikov ◽  
V. Yu. Tsviatkou ◽  
A. P. Shkadarevich
Keyword(s):  
Computational Complexity ◽  
Image Quality ◽  
Dynamic Range ◽  
Histogram Equalization ◽  
Brightness Distribution ◽  
Adaptive Equalization ◽  
Infrared Images ◽  
Image Reproduction ◽  
Adaptive Histogram Equalization

The problem of reducing the dynamic range and improving the quality of infrared (IR) images with a wide dynamic range for their display on a liquid crystal matrix with 8-bit pixels is considered. To solve this problem in optoelectronic devices in real time, block algorithms based on local equalization of the histogram are widely used, taking into account their relatively low computational complexity and the possibility of taking into account local features of the brightness distribution. The basic adaptive histogram equalization algorithm provides reasonably high image quality after conversion, but may result in excessive contrast for some types of images. In a modified algorithm of adaptive histogram equalization, the contrast is limited by a threshold by truncating local maxima at the edges of the histogram. This leads, however, to a deterioration in other indicators of image quality. This disadvantage is inherent in many algorithms of local histogram equalization, along with limited control over the characteristics of image reproduction quality. To improve the quality and expand the control interval for the characteristics of the reproduction of infrared images, the article proposes an algorithm for double reduction of the dynamic range of the image with intermediate control of the shape of its histogram. This algorithm performs: preliminary reduction of the dynamic range of the image based on adaptive equalization of the histogram, control of the shape of the histogram based on its linear or nonlinear compression, linear stretching of its central part and linear stretching (compression) of its lateral parts, final reduction of the dynamic range based on linear compression of the entire histograms. The characteristics of the proposed algorithm are compared with the characteristics of known algorithms for reducing the dynamic range and improving the image quality. The dependences of the characteristics of the quality of image reproduction after a decrease in their dynamic range on the control parameters of the proposed algorithm and recommendations for their choice taking into account the computational complexity are given.

scholarly journals Image Defogging Framework Using Segmentation and the Dark Channel Prior

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 285
Author(s):  
Sabiha Anan ◽  
Mohammad Ibrahim Khan ◽  
Mir Md Saki Kowsar ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
...  
Keyword(s):  
Histogram Equalization ◽  
Homogeneous Region ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Color Information ◽  
Visual Effects ◽  
Low Contrast ◽  
Adaptive Histogram Equalization ◽  
Processing Step ◽  
Dark Channel

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.

scholarly journals WAVELET SHRINKAGE ADAPTIVE HISTOGRAM EQUALIZATION FOR MEDICAL IMAGES

2014 ◽  
pp. 191-196
Author(s):  
Anbu Megelin star ◽  
Perumal Subburaj
Keyword(s):  
Medical Image ◽  
Medical Image Processing ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Image Brightness ◽  
Adaptive Histogram Equalization ◽  
Approximation Coefficients ◽  
Medical Image Enhancement ◽  
Novel Algorithm

Enhancement techniques play a major role in medical image processing, to improve the quality of raw images. This paper proposes a novel algorithm namely wavelet shrinkage adaptive histogram equalization (WSAHE) for medical image enhancement. This algorithm consists of four stages namely, decomposition of images using wavelet transform, application of adaptive histogram equalization on the approximation coefficients, application of shrinkage on the detailed coefficients and the reconstruction of image. Experiments show that the proposed method enhances the image brightness while preserving edges.

scholarly journals De-noising of MRI Images in Wavelet Domain

2019 ◽  
Vol 8 (4) ◽  
pp. 1947-1949
Keyword(s):  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
The Body ◽  
Medical Procedure ◽  
Wavelet Domain ◽  
Radio Waves ◽  
Resonance Imaging ◽  
Adaptive Histogram Equalization ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) is a diagnostic medical procedure that utilizes solid attractive fields and radio waves to deliver definite pictures of within the body. Extensive research has been completed into whether the attractive fields and radio waves utilized during MRI sweeps could represent a hazard to the human body. No proof has been found to propose there's a hazard, which means MRI outputs are one of the most secure restorative methodology accessible. MRI has several advantages which make it ideal in numerous situations, in particular, it can identify small changes of structures inside the body. The disadvantage is the noise that degrades the quality of the image. A threestep processing algorithm is proposed to reduce this noise. Here, first it includes soft thresholding in wavelet domain where the original image is divided into blocks that do not overlap. Then it includes restoration of the object boundaries and texture which are lost as a result of the first step and finally enhancing the image using CLAHE (Contrast Limiting Adaptive Histogram Equalization). It is then analyzed using the error parameters like peak signal to noise ratio and mean square error.

scholarly journals Contrast Enhancement of Mammograms and Microcalcification Detection

2019 ◽  
Vol 8 (4) ◽  
pp. 3926-3932
Keyword(s):  
Operative Procedure ◽  
Histogram Equalization ◽  
Local Contrast ◽  
Edge Information ◽  
Edge Structure ◽  
Low Contrast ◽  
Structure Information ◽  
Enhancement Method ◽  
Microcalcification Detection ◽  
Mammogram Images

Mammography is an operative procedure for early detection of cancer present in breast. However, the pathological changes of the breast are difficult to interpret from low contrast mammograms. This research proposes a method to enhance the contrast of the mammogram that uses Non-subsampled contourlet transform (NSCT) based edge information. Instead of a directional filter bank in the conventional NSCT structure, this paper uses multiscale non-separable edge filters. These edge filters outputs intrinsic edge structure information based on simplified hyperbolic tangent function applied with two polarized schemes. This edge information further used to improve the local contrast. Adaptive histogram equalization (AHE) also used to increase the overall contrast of mammogram. Improved detection of microcalcification (MC) from enhanced mammogram images shows the success of this algorithm. This method has better enhancement measure (EME) than AHE and unsharp based mammogram enhancement method.

scholarly journals Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)

2018 ◽  
Vol 16 (37) ◽  
pp. 127-135
Author(s):  
Loay Kadom Abood
Keyword(s):  
Contrast Enhancement ◽  
Infrared Imaging ◽  
Experimental Tests ◽  
Histogram Equalization ◽  
Night Vision ◽  
Infrared Images ◽  
Adaptive Histogram Equalization ◽  
General Quality ◽  
Improvement Procedures

The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed significant improvements.

Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android

2020 ◽  
Vol 12 (2) ◽  
pp. 80-88
Author(s):  
Claudia Kenyta ◽  
Daniel Martomanggolo Wonohadidjojo
Keyword(s):  
Image Enhancement ◽  
Light Condition ◽  
Histogram Equalization ◽  
Low Light ◽  
Adaptive Histogram Equalization ◽  
Enhancement Method ◽  
Better Than

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.

Sign in / Sign up

Export Citation Format

Share Document