Weighted Transformation and Wavelet Transforms-Based Image Resolution and Contrast Enhancement

Kumari, N. Prasanthi; Laksmi, T. V. Hyma; Bhavani, Dr.R.; Durga, G.

doi:10.1007/978-981-10-5903-2_179

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…The regions of the image with low brightness are enhanced with increasing α value to attain uniform brightness over the entire image. The weighted average of the input and enhanced image are used to compensate for the over brightness [10] and is represented as α is a regulatory exponent. min and max are the extreme limits of α.…”

Section: Proposed Methodsmentioning

confidence: 99%

I_{Comp} = I_{low} . M_{α} + β . I_{CE} (One {normals}_{Matrix} - M_{α})

where I Comp = brightness compensated enhanced image I low = low contrast image or input image β = brightness compensation factor I CE = contrast enhanced image Ones matrix = matrix of all ones with the same size of the input image M α = weighing coefficients matrix with the same size of the input image M α ( i , j ) = ( I C ( i , j )) α …”

Section: Proposed Methodsmentioning

confidence: 99%

“…Resolution enhancement (RE) increases the number of pixels in an image. Bicubic interpolation is widely used in RE, in which the intensity value of the unknown pixel is assigned by the weighted average of the sixteen surrounding pixels [8][9][10]. Further, in recent times, it is found that the wavelet transforms (WT) [11,12] play an important role in decomposing the image into different frequency sub-bands.…”

Section: Introductionmentioning

confidence: 99%

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Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Tirumani

Tenneti

K³

et al. 2021

IET Image Processing

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Improving the image resolution and contrast along with uniform brightness distribution over the entire image helps to retrieve the vital realistic information necessary for human perception and interpretation. A new procedure to improve these parameters is implemented and tested. Initially, the image is super resolute using discrete wavelet transform (DWT), stationary wavelet transform (SWT) image decomposition and bicubic interpolation. The resolute image is used for contrast enhancement by using SWT and contrast limited adaptive histogram equalization (CLAHE) approach. The optimized brightness compensation technique, which uses particle swarm optimization (PSO), is applied to the resolution and contrast-enhanced image to obtain uniform brightness distribution over the entire image. The proposed approach is tested on three data sets of images and it is found that the visual results, as well as the resolution and contrast levels of the tested images, are significantly superior to subjective and objective results.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Proposed Methodsmentioning

confidence: 99%

I_{Comp} = I_{low} . M_{α} + β . I_{CE} (One {normals}_{Matrix} - M_{α})

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Tirumani

Tenneti

K³

et al. 2021

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Compared to various other algorithms, the SDS has a mathematical model which is robust and this describes the behavior of a technique found in the researching of allotment of resources, its global optimal convergence, a condition of minimum convergence, linear time complexity and its robustness. [12]. The work has suggested a wavelet filter bank method of optimization that has produced a better filter set which is problem-specific using the Stochastic Diffusion Searches (SDSs) that can discover the predetermined patterns and their location.…”

Section: Stochastic Diffusion Search(sds)mentioning

confidence: 99%

Satellite Image Enhancement using Optimized Wavelet Decomposition and Bicubic Interpolation

Lakshmi*¹,

Kavya²,

Madhu³

et al. 2020

IJRTE

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Satellite Images (SI) play a vital role in various civilian and military applications for weather forecasting, monitoring of resources of the earth, environmental studies, observing natural disasters and natural calamities, etc. When these SI are used in military applications and almost all other applications for efficient study, the big challenge is its resolution. In wavelet transforms based satellite image enhancement techniques, choosing a proper wavelet transform plays a key role and vary with the image to image. To improve the resolution, a novel robust optimized wavelet decomposition and a bicubic interpolation-based satellite image enhancement method is proposed. In this method, the Stochastic Diffusion Search (SDS) algorithm is used to get the optimized wavelet decomposition of the image into different subbands and bicubic interpolation is used to improve the resolution. Image is decomposed using the optimized wavelet filter bank based on the SDS algorithm, decomposed sub-bands are interpolated with bicubic interpolation and inverse wavelet transform is applied to compose the interpolated sub-bands into a high-resolution image. The proposed method is tested on satellite images and other images also. Compared to the proposed method with the existing methods and proved that the proposed method is superior to existing methods and applicable to any type of image.

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“…The selection of SB size and clip limit is crucial for CLAHE because these parameters mainly control image quality. These parameters of CLAHE [13] (1) CLAHE-DWT: This method uses the combination of CLAHE and DWT to eliminate the drawbacks of CLAHE. By applying CLAHE to detailed coefficients can reduce the noise.…”

Section: B Clahementioning

confidence: 99%

Gaussian Mixture Model and Lifting Wavelet Transformed Base Satellite Image Enhancement

Lakshmi¹,

Madhu²,

Kavya³

2019

IJRTE

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From the last few decades, Satellite images are being used widely in various applications like monitoring of forest areas, weather forecasting, polar bears counting, etc. In those applications to get more details of images efficiently, satellite images should be enhanced up to the required level as the images captured by the satellites are covered very large areas and those are very low-resolution images due to the high altitudes of satellites from the earth. We proposed a method of an image enhancement which includes both resolution enhancement and contrast enhancement. In this method, Stationary Wavelet Transform (SWT) in combination with Lifting Wavelet Transform (LWT) is used for image decomposition into low-frequency sub band images and high-frequency sub band images to separate smooth regions and sharp edges to interpolate regions and edges separately to reduce blurring effect in edges and noise in smooth regions. To get smoother details and sharper edges, Gaussian Mixture Model (GMM) is used for interpolation in resolution enhancement process and SWT with the combination of Contrasts Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement process. SWT in combination with LWT improves the resolution effectively and also minimizes the execution time drastically than existing methods due to the shift invariance of SWT and reduced computations in LWT and GMM interpolation results from sharper edges and smoother details. SWT is used in combination with CLAHE to enhance the contrast and mitigate the noise effects than existing methods. The proposed method gives superior results and compared with existing techniques with PSNR, Noise Estimation, and visual results

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Weighted Transformation and Wavelet Transforms-Based Image Resolution and Contrast Enhancement

Cited by 4 publications

References 5 publications

Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

Satellite Image Enhancement using Optimized Wavelet Decomposition and Bicubic Interpolation

Gaussian Mixture Model and Lifting Wavelet Transformed Base Satellite Image Enhancement

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