Wavelet based denoising by correlation analysis for high dynamic range imaging

Kaftan, Jens N.; Bell, André A.; Seiler, Claude; Aach, Til

doi:10.1109/icip.2009.5414054

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…Porlilla and Simoncelli used wavelet self-correlation at each layer to perform denoising with a priori statistical parameters of the noise [25] . Kaftan et al used the correlation between multiple images to perform denoising [26] . Therefore, the correlation method can preserve the inherent feature well.…”

Section: State-of-the-art Wavelet Thresholdingmentioning

confidence: 99%

Novel wavelet self-optimisation threshold denoising method in axle press-fit ultrasonic defect detection

Peng¹,

Gao²,

Wang³

2016

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Novel wavelet self-optimisation threshold denoising method in axle press-fit ultrasonic defect detectionAxles are one of the most important parts of railway locomotives and vehicles. The periodic ultrasonic inspection of axles can effectively detect and monitor axle fatigue cracks. However, in the wheel-seat press-fit zone, the complex interface contact condition reduces the signal-to-noise ratio (SNR). Therefore, the probability of false positives and false negatives increases. A novel wavelet threshold function is created to remove noise and suppress press-fit interface echoes in ultrasonic axle defect detection. Self-optimisation of the function is proposed using the positive correlation between the correlation coefficient and SNR. The performance of the proposed method is assessed using real data and compared with traditional threshold methods and a state-of-the-art threshold. The statistic results of the amplitude and peak SNR of defect echoes show that the proposed self-optimisation of the wavelet threshold denoising method not only maintains the amplitude of defect echoes but also provides a higher peak SNR.

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Section: State-of-the-art Wavelet Thresholdingmentioning

confidence: 99%

Novel wavelet self-optimisation threshold denoising method in axle press-fit ultrasonic defect detection

Peng¹,

Gao²,

Wang³

2016

Insight

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“…Step 3 -Post processing of the image: This step results in noise or artifact removal. Various artifact removal methods have been developed to date by different researchers [8][9][10][11][12][13][14]. Depending on the applications or requirements Step 2 and Step 3 can be interchanged.…”

Section: Introduction 11 Introductionmentioning

confidence: 99%

Human visual system inspired saliency guided edge preserving tone-mapping for high dynamic range imaging

Barai¹

2021

Preprint

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With the growing popularity of High Dynamic Range Imaging (HDRI), the necessity for advanced tone-mapping techniques has greatly increased. In this thesis, I propose a novel saliency guided edge-preserving tone-mapping method that uses saliency region information of an HDR image as input to a guided filter for base and detail image layer separation. Both high resolution and low resolution saliency maps were used for the performance evaluation of the proposed method. After detail layer enhancement and base layer compression with constant weights, a new edge preserved tone-mapped image was composed by adding the layers back together with saturation and exposure adjustments. The filter operation is faster due to the use of the guided filter, which has O(N) time operation with N number of pixels. Both objective and subjective quality assessment results demonstrated that the proposed method has higher edge and naturalness preserving capability, which is homologous to the Human Visual System (HVS), as compared to other state-of-the-art tone-mapping approaches.

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