Satellite Image Enhancement using Discrete Wavelet Transform, Singular Value Decomposition and its Noise Performance Analysis

Sharma, Aditi; Khunteta, Ajay

doi:10.1109/icmete.2016.32

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…Perform the Akamatsu y-direction transform on the LH subband, the x-direction on the HL subband and the xy-direction on the HH subband. This is done because the LH subband has a middle frequency horizontally, HL has a vertically middle frequency and HH has a diagonal high frequency [6,13,21,25]. Table 2 shows the results and PSNR values of the reconstructed images from the proposed restoration method.…”

Section: Joint Methods For Image Restorationmentioning

confidence: 99%

“…LL is representative of the image and is the sub-band most similar to the original image before it is decomposed. LH, HL, and HH are wavelets of variations vertically, horizontally, and diagonally [6,13,21,25]. Decomposition on DWT can be done using Eqs.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Decomposition using wavelet transform produces two-dimensional functions of time and space. Wavelet decomposition is also helpful in recognizing singularity details by analyzing the time frequencies of non-stationary signals [21][22][23]. Furthermore, outside there have been Joint method for image restoration many forms of developed wavelet transforms and each has different characteristics.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

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Joint method using Akamatsu and discrete wavelet transform for image restoration

Akbar

Setiadi

2020

ACI

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Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.

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Section: Joint Methods For Image Restorationmentioning

confidence: 99%

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Section: Discrete Wavelet Transformmentioning

confidence: 99%

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Joint method using Akamatsu and discrete wavelet transform for image restoration

Akbar

Setiadi

2020

ACI

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“…As said earlier the proposed method is just an improvement of the conventional DWT-SVD-GHE based method and the proposed method is using concept of applying de-noising filter at very beginning of the processing from the work of [18]. The DWT-SVD-GHE method does not use any method to remove noise from the input image also the GHE method suffers from the mean shift problem.…”

Section: -The Proposed Methodsmentioning

confidence: 99%

Noisy Satellite Image Contrast Enhancement Using Median Filter, DWT–SVD And MMBEBHE Based Method

Gupta¹,

Agarawal²

2017

International Journal of Innovative Research and Growth

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“…For example, low-light image enhancement is performed by wavelet. Wavelet transform has been widely used in the field of image processing because of its excellent time-frequency localization and multi-resolution analysis ability [13][14][15]. The use of stationary wavelet transform for sharpening to enhance the details of the image.…”

Section: Introductionmentioning

confidence: 99%

Low Illumination Video Image Enhancement

Yang

Kasabov

2020

IEEE Photonics J.

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Due to weather conditions, brightness conditions, capture equipment and other factors, leads to video unclear or even abnormally confused, which is not conducive to monitoring, and can not meet the needs of applications. Based on the actual data of night video surveillance, this paper proposes a new low illumination video image enhancement algorithm, which overcomes the existing problems. We analyze the characteristics of low illumination video image, and use HSV color space instead of traditional RGB space to enhance the robustness of video contrast and color distortion. At the same time, we use wavelet image fusion to highlight the details of video image, so the enhanced video has higher clarity and visual effect. Compared with other four algorithms, the proposed algorithm outperforms the above algorithms in subjective evaluation and objective evaluation. At the same time, compared with other algorithms, the proposed algorithm has faster processing time for each frame. Experiments show that the algorithm can effectively improve the overall brightness and contrast of video images, and avoid the over-enhancement of bright areas near the light source, which can meet the practical application requirements of video surveillance.

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Satellite Image Enhancement using Discrete Wavelet Transform, Singular Value Decomposition and its Noise Performance Analysis

Cited by 14 publications

References 12 publications

Joint method using Akamatsu and discrete wavelet transform for image restoration

Joint method using Akamatsu and discrete wavelet transform for image restoration

Noisy Satellite Image Contrast Enhancement Using Median Filter, DWT–SVD And MMBEBHE Based Method

Low Illumination Video Image Enhancement

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