A Fusion of Cuckoo Search and Multiscale Adaptive Smoothing Based Unsharp Masking for Image Enhancement

2019 ◽  
Vol 10 (3) ◽  
pp. 151-174 ◽  
Author(s):  
Lalit Maurya ◽  
Prasant Kumar Mahapatra ◽  
Amod Kumar
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Adaptive Smoothing ◽  
Visual Appearance ◽  
Search Optimization ◽  
Fusion Images ◽  
Contrast Stretching ◽  
Linear Contrast

Image enhancement means to improve the visual appearance of an image by increasing its contrast and sharpening the features. This article presents a fusion of cuckoo search optimization-based image enhancement (CS-IE) and multiscale adaptive smoothing based unsharping method (MAS-UM) for image enhancement. The fusion strategy is introduced to improve the deficiency of enhanced image that suppresses the saturation and over-sharpness artefacts in order to obtain a visually pleasing result. The ideology behind the selection of fusion images (candidate) is that one image should have high sharpness or contrast with maximum entropy and other should be high Peak Signal-to-Noise Ratio (PSNR) sharp image, to provide a better trade-off between sharpness and noise. In this article, the CS-IE and MAS-UM results are fused to combine their complementary advantages. The proposed algorithms are applied to lathe tool images and some natural standard images to verify their effectiveness. The results are compared with conventional enhancement techniques such as Histogram equalization (HE), Linear contrast stretching (LCS), Contrast-limited adaptive histogram equalization (CLAHE), standard PSO image enhancement (PSO-IE), Differential evolution image enhancement (DE-IE) and Firefly algorithm-based image enhancement (FA-IE) techniques.

An Improved Cuckoo Search based Optimal Ranged Brightness Preserved Histogram Equalization and Contrast Stretching Method

2017 ◽  
Vol 8 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Optimal Range ◽  
Novel Variant ◽  
Contrast Stretching

This paper is organized into two main parts. In the first part, two methods have been discussed to preserve the original brightness of the image which are Parameterized transformation function and a novel variant of modified Histogram Equalization (HE) method. In this study both methods have been formulated as optimization problems to increase the efficiency of the corresponding methods within reasonable time. In the second part, a novel modified version of Cuckoo Search (CS) algorithm has been devised by using chaotic sequence, population diversity information etc to solve those formulated optimization problems. A new Co-occurrence matrix's features based objective function is also devised to preserve the original brightness. Peak-signal to noise ratio (PSNR) acts as objective function to find optimal range of enhanced images. Experimental results prove the supremacy of the proposed CS over traditional CS algorithm.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Gradient, Texture Driven Based Dynamic-Histogram Equalization For Medical Image Enhancement

2021 ◽  
Vol 15 ◽  
pp. 303-310
Author(s):  
H. N. Vidyasaraswathi ◽  
M. C. Hanumantharaju
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Medical Images ◽  
Similarity Index ◽  
Mapping Function ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Visual Appearance ◽  
Index Measurement ◽  
Medical Image Enhancement

In many clinical diagnostic measurements, medical images play some significant role but often suffer from various types of noise and low-luminance, which causes some notable changes in overall system accuracy with misdiagnosis rate. To improve the visual appearance of object regions in medical images, image enhancement techniques are used as potential pre-processing techniques. Due to its simplicity and easiness of implementation, histogram equalization is widely preferred in many applications. But due to its mapping function based image transformation during enhancement process affect the biomedical patterns which are essential for diagnosis. To mitigate these issues in medical images, a new method based on gradient computations and Texture Driven based Dynamic histogram equalization (GTDDHE) is accomplished to increase the visual perception. The spatial texture pattern is also included to ensure the texture retention and associated control over its variations during histogram modifications. Experimental results on MRI, CT images, eyes images from medical image datasets and quantitative analysis by PSNR, structural similarity index measurement (SSIM), information entropy (IE) and validated that the proposed method offers improved quality with maximum retention of biomedical patterns across all types of medical images.

scholarly journals Brain MR Image Enhancement using Average Intensity Replacement Based on GWOHE Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 3193-3199
Keyword(s):  
Image Enhancement ◽  
Visual Image ◽  
Histogram Equalization ◽  
Average Intensity ◽  
Low Grade ◽  
Mr Image ◽  
Brain Images ◽  
Visual Appearance ◽  
Processing Techniques ◽  
Mri Brain Images

The most important task in MR Image Enhancement is to obtain a high resolution optimized visual image using advanced image processing techniques. Most of the life photographs and various images such as aerial, medical and satellite are associated with noise and low grade intensity. To improve the quality for better visual appearance, noise has to be suppressed and contrast has to be enhanced. Traditional contrast improvement techniques do best for various images. But for MRI of brain images, there are chances of misrecognization of WMH (White Matter Hyperintensities) as Cerebrospinal fluid (CSF) in traditional enhancement techniques. To overcome this ambiguity and enhance WMH regions of MRI brain images, a novel algorithm has been proposed in this paper. This algorithm is called as Mean Intensity replacement based on Grey Wolf Optimization Histogram Equalization (GWOHE). This technique is applied on FLAIR images and comparison is tabulated along with existing technique for parameters such as PSNR, AMBE.

scholarly journals Spatial domain image enhancement techniques for acute myeloid leukemia (M1,M4,M5,M7)

2019 ◽  
Vol 14 (1) ◽  
pp. 250
Author(s):  
A.S. A.Salam ◽  
M.N. M.Isa ◽  
M. I. Ahmad
Keyword(s):  
Acute Myeloid Leukemia ◽  
Image Enhancement ◽  
Myeloid Leukemia ◽  
Signal To Noise Ratio ◽  
Spatial Domain ◽  
Signal To Noise ◽  
Image Subtraction ◽  
Image Sharpening ◽  
Contrast Stretching ◽  
Acute Myeloid

<p class="Abstract"><span>In this paper, several techniques of image enhancement spatial domain is elucidated and analyzed for the purpose of enhancing Acute Myeloid Leukemia (AML) subtype of M1, M4, M5 and M7. The techniques involved contrast stretching of greyscale images, image subtraction and image sharpening. The three methods compared with one another to achieve the highest PSNR value for the suitability technique of AML subtypes (M1, M4, M5 and M7). Firstly, subtypes images converted into grayscale. Then, each four images tested with contrast stretching techniques followed by image subtraction and image sharpening. The performances were evaluated based on Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Due to its higher value obtained, image sharpening is a good enhancement techniques for Acute Myeloid Leukemia with 68.2083 dB and the lowest MSE achieved of 0.0103.</span></p>

Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

2015 ◽  
Vol 5 (4) ◽  
pp. 45-73 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sanjoy Das
Keyword(s):  
Artificial Bee Colony ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Population Diversity ◽  
Objective Functions ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Values ◽  
Occurrence Matrix ◽  
Contrast Stretching

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching (ORBPCS) method and weighted thresholded histogram equalization (WTHE) method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony (ABC) algorithm and a novel objective function have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective functions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective functions have been developed by using co-occurrence matrix and peak-signal to noise ratio (PSNR).

Optimization Before Biomedical Image Compression Using CLAHE and DCS

2018 ◽  
Vol 7 (3.27) ◽  
pp. 236
Author(s):  
Satyawati S. Magar ◽  
Bhavani Sridharan
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Image Enhancement ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Biomedical Image ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

In current years, improving the Compression Ratio (CR) in medical imaging is essential and becomes big challenge in the field of biomedical. In that direction we have done optimization before biomedical image compression. For the same we have used the image enhancement techniques. For the enhancement of an image we have used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation Stretch (DCS) algorithms. By optimizing an image before compression we have achieved better Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) than existing methods of an image compression. Mainly results are compared with Oscillation Concept method of an image compression with and without optimization.  

INVESTIGATION OF HISTOGRAM EQUALIZATION FILTER FOR CT SCAN IMAGE ENHANCEMENT

2019 ◽  
Vol 31 (05) ◽  
pp. 1950038
Author(s):  
Ayesha Amir Siddiqi ◽  
Ghous Bakhsh Narejo ◽  
Mashal Tariq ◽  
Adnan Hashmi
Keyword(s):  
Image Enhancement ◽  
Quantum Noise ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Noise Removal ◽  
Ct Images ◽  
Liver Tumour ◽  
Clinical Images ◽  
Computed Tomographic ◽  
Equalization Method

This piece of work investigates the application of histogram equalization method to clinical images for noise removal and efficient image enhancement without any information loss. Computed tomographic (CT) images of the abdomen bearing liver tumour are kept under study. Liver exhibits heterogeneous combination of intensities which makes it a challenging task to enhance the liver tumour embedded in the image. Distortion occurs due to the presence of quantum noise in the CT scans and important information of the image is suppressed. The methodology adopted in this paper comprises of two stages. Initially pixel based intensity transformation is adopted for de-noising the background of the image by the selection of appropriate threshold levels. The resultant image gives a noise free background and the foreground features are enhanced. In the next stage histogram equalization filters are applied to the transformed image. The equalization method which gives uniform image enhancement with lesser mean square error (MSE) and increased peak signal to noise ratio (PSNR) is supposed to be an effective method for efficient enhancement of the images. This study deals with the application of histogram equalization methods to CT images which can aid the radiologists for better visualization and diagnosis of the disease.

scholarly journals COMPARISON OF IMAGE ENHANCEMENT METHODS FOR CHROMOSOME KARYOTYPE IMAGE ENHANCEMENT

2017 ◽  
Vol 10 (1) ◽  
pp. 50 ◽  
Author(s):  
Dewa Made Sri Arsa ◽  
Grafika Jati ◽  
Agung Santoso ◽  
Rafli Filano ◽  
Nurul Hanifah ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Histogram Equalization ◽  
Block Matching ◽  
Structural Similarity Index ◽  
Method Performance ◽  
Enhancement Method ◽  
Basic Image

The chromosome is a set of DNA structure that carry information about our life. The information can be obtained through Karyotyping. The process requires a clear image so the chromosome can be evaluate well. Preprocessing have to be done on chromosome images that is image enhancement. The process starts with image background removing. The image will be cleaned background color. The next step is image enhancement. This paper compares several methods for image enhancement. We evaluate some method in image enhancement like Histogram Equalization (HE), Contrast-limiting Adaptive Histogram Equalization (CLAHE), Histogram Equalization with 3D Block Matching (HE+BM3D), and basic image enhancement, unsharp masking. We examine and discuss the best method for enhancing chromosome image. Therefore, to evaluate the methods, the original image was manipulated by the addition of some noise and blur. Peak Signal-to-noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used to examine method performance. The output of enhancement method will be compared with result of Professional software for karyotyping analysis named Ikaros MetasystemT M . Based on experimental results, HE+BM3D method gets a stable result on both scenario noised and blur image. 

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