LAPB: Locally adaptive patch-based wavelet domain edge-preserving image denoising

Jain, Paras; Tyagi, Vipin

doi:10.1016/j.ins.2014.09.060

Cited by 56 publications

(29 citation statements)

References 47 publications

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“…That is, time windows and frequency windows can have a dynamic adjustment, according to the specific signal forms. Due to the multi-resolution capability of the wavelet transform, the wavelet theory has been widely applied in the research of image processing such as image compression [19], image de-noising [20], image fusion [21] and so on.…”

Section: The Improved Wavelet Fuzzy Threshold De-noising Methodsmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the denoising of night vision images, which lay the foundation for apple harvesting robots working at night.

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Section: The Improved Wavelet Fuzzy Threshold De-noising Methodsmentioning

confidence: 99%

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Ruan

Zhao

Jia

et al. 2015

Int. j. adv. robot. syst.

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show abstract

“…Jain and Tyagi [13] have also proposed very recently a new technique for noise reduction in wavelet domain. They presented a new locally adaptive patch-based (LAPB) thresholding scheme that relies on the aggregation of multiple thresholded estimates of a wavelet coefficient and involves estimation of thresholding parameters for a wavelet coefficient (such as signal variance) in a local neighborhood.…”

Section: Related Workmentioning

confidence: 99%

“…Reconstruction from these modified coefficients then gives the desired denoised image. Numerous denoising methods follow such procedure of wavelet thresholding [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Such variance estimation is popular for various denoising methods and achieves efficient denoising results with a low complexity. Numerous denoising approaches [10][11][12][13]20] have taken benefit of joint statistical relationships among the wavelet coefficients by estimating the variance of a coefficient adaptively from a local neighborhood window consisting of coefficients within a subband. The size of the locally adaptive window also plays an important role in estimating the signal variance.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain

Jain¹,

Tyagi²

2015

IJCA

Self Cite

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Image denoising is a well explored topic in the field of image processing. A denoising algorithm is designed to suppress the noise while preserving as many image structures and details as possible. This paper presents a novel technique for edgepreserving image denoising using wavelet transforms. The multi-level decomposition of the noisy image is carried out to transform the data into the wavelet domain. An adaptive thresholding scheme which employs arbitrary shaped local windows and is based on edge strength is used to effectively reduce noise while preserving significant features of the original image. The experimental results, compared to other approaches, prove that the proposed method is suitable for various image types corrupted by Gaussian noise.

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“…Preservation of original information and not introducing additional artifacts while reducing noise component is one of the main challenges in image denoising. A wide variety of algorithms have been proposed over the past few decades, including various filtering-based spatial methods [28][29], transform domain methods [25][26][27], wavelet thresholding-based approaches [30] and total-variation (TV)-based approaches [31] . Stateof-the-art is represented by BM3D [32], centralized sparse representation (CSR) [33] and learned simultaneous sparse coding (LSSC) [34].…”

Section: Introductionmentioning

confidence: 99%

Optimized Parameters for Bell-Shaped Error Function in Image Denoising

Özkan¹,

Seke²

2017

ANADOLU UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY a - Applied Sciences and Engineering

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Adaptive image denoising algorithms rely on an error function that measure the distance between an estimated result and expectations. Selection of the error function and its parameters are crucial for a successful denoising implementation. In this paper, a method for determining close-to-optimal parameters for a bell-shaped error function is evaluated. The function with calculated parameters is employed within a gradient optimization algorithm and tested using test images with varying noise types and levels. The restoration results of the denoising test runs that use the proposed parameters are compared against the results of algorithms that employ well-known least squares and sum of absolute differences methods along with a method that combines both. The clear superiority of the bell-shaped error function for the proposed parameters is shown by the test results.

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LAPB: Locally adaptive patch-based wavelet domain edge-preserving image denoising

Cited by 56 publications

References 47 publications

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Night Vision Image De-noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold

Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain

Optimized Parameters for Bell-Shaped Error Function in Image Denoising

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