Image Enhancement Method Based on Bilinear Interpolating and Wavelet Transform

Ye, Hanmin; Su, Keqin; Huang, Shiming

doi:10.1109/iaeac50856.2021.9390624

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…This drawback appears because the Fourier transform is localized just in the frequency domain, while the wavelet transforms reasonably localized in the time and frequency domains, and the time details that are beneficial in numerous contexts can't be lavish. Wavelet gives outstanding performance in removing noise from the digital images due to the features such as sparsity and multiresolution configuration [Saluja,2015;Ye, 2021].…”

Section: Wavelet Transform Domainmentioning

confidence: 99%

“…The high-frequency sub-band is correlated and manipulated with the threshold without affecting the points of the lowfrequency wavelet band. The threshold approach and inverse wavelet transformation are then used to obtain the enhanced image [Ferzo, 2020;Ye, 2021].…”

Section: Wavelet Transform Domainmentioning

confidence: 99%

“…For assemble additional benefits, the image needs to improve the proper details of the image. Image improvement is a significantly meaningful image procedure in digital image processing, in order to display the components of noisy images [Ye, 2021]. So, image quality plays a critical Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transform role to determine the capacity to get thorough details from obtained images in many areas that deal with digital images [Mustafa, 2021].…”

Section: Introductionmentioning

confidence: 99%

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Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Mustafa

2022

ACAD J NAWROZ UNIV

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In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance. Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques. In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.

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Section: Wavelet Transform Domainmentioning

confidence: 99%

Section: Wavelet Transform Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Mustafa

2022

ACAD J NAWROZ UNIV

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“…Many image scaling methods have been proposed recently , and these methods can be divided into two categories, one being sample-based super-resolution reconstruction [4][5][6][7][8][9][10][11][12] and the other being sample-free-based interpolation [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. The main difference between the two is that sample-free-based interpolation uses mathematical methods to estimate pixels directly based on the known pixels, whereas super-resolution reconstruction requires training samples to establish a mapping relationship between low-resolution images and high-resolution images before it can use image block-matching and replacement to complete the interpolation.…”

Section: Introductionmentioning

confidence: 99%

“…Nonlinear interpolation methods mainly include methods based on wavelet transform [25][26][27][28] and methods based on edge information [13][14][15][16][17][18][19][20][21][22][23][24]. The interpolation is based on wavelet transform: first, wavelet transform is performed on the image; next, the classical image interpolation method is used to interpolate the frequency domain coefficients; then, threshold processing is performed to obtain the required interpolation image.…”

Section: Introductionmentioning

confidence: 99%

Image Interpolation with Regional Gradient Estimation

Jia

Huang

2022

Applied Sciences

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This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

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Fast Automatic Registration of UAV Images via Bidirectional Matching

Luo,

Wei,

Jin

et al. 2023

Sensors

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Image registration plays a vital role in the mosaic process of multiple UAV (Unmanned Aerial Vehicle) images acquired from different spatial positions of the same scene. Aimed at the problem that many fast registration methods cannot provide both high speed and accuracy simultaneously for UAV visible light images, this work proposes a novel registration framework based on a popular baseline registration algorithm, ORB—the Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elemental Features) algorithm. First, the ORB algorithm is utilized to extract image feature points fast. On this basis, two bidirectional matching strategies are presented to match obtained feature points. Then, the PROSRC (Progressive Sample Consensus) algorithm is applied to remove false matches. Finally, the experiments are carried out on UAV image pairs about different scenes including urban, road, building, farmland, and forest. Compared with the original version and other state-of-the-art registration methods, the bi-matching ORB algorithm exhibits higher accuracy and faster speed without any training or prior knowledge. Meanwhile, its complexity is quite low for on-board realization.

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Image Enhancement Method Based on Bilinear Interpolating and Wavelet Transform

Cited by 3 publications

References 6 publications

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

Image Interpolation with Regional Gradient Estimation

Fast Automatic Registration of UAV Images via Bidirectional Matching

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