Wide-stripe noise removal method of hyperspectral image based on fusion of wavelet transform and local interpolation

Huang, Shiqi; Liu, Zhigang; Wang, Yiting; Wang, Rongrong

doi:10.1007/s10043-017-0318-y

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…The article [24] introduces an algorithm based on media filter and wavelet transform, a set of processes based on wavelet transform are introduced in article [25], but the main goal is not stripe noise. An algorithm for wide-stripe noise is introduced in article [26]. Article [27] proposes an algorithm by wavelet moment matching and it assumes that the stripe noise is caused by the different gain and offset of the sensors.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

Chen¹,

Feng

Wu³

et al. 2019

IEEE Access

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Advanced Geostationary Radiation Imager (AGRI) is one of the main payloads of the second-generation geostationary orbit meteorological satellite, FengYun-4A. Typically, the existence of variable stripe noise in the water vapor band remote sensing images of the AGRI greatly affects many applications, such as cloud detection, especially as one full disk image is separated into ten sub-images for transforming as soon as possible, so the denoising algorithm, which can reduce variable stripe noise and is adaptive to process using sub-images, must be built. In this paper, we propose an adaptive wavelet filter for image denoising. This approach introduces a new parameter termed weight sum variance of digital number probability (WSVODP), which is used to indicate the appropriate wavelet filter coefficients. WSVODP is only sensitive to the difference of observation targets of different sensors. Thus, our approach can learn appropriate wavelet filter coefficients fast and exactly. We built a real-world remote sensing image dataset from AGRI on FengYun-4A, and the experimental results on this dataset show that the proposed approach could effectively reduce the variable stripe noise from different observation targets. At the same time, an edge compensation method, which is fitted to the scanning model of the AGRI, is suggested to avoid ringing artifacts. Many applications, such as cloud detection with denoised images, show very good results. The proposed approach reduces the stripe noise adaptation, so the result is very steady even if the stripe noise varies with different targets, and edge compensation ensures that there are no obvious ringing artifacts in the full disk image joined by the ten sub-images.

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Section: Related Workmentioning

confidence: 99%

Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

Chen¹,

Feng

Wu³

et al. 2019

IEEE Access

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show abstract

“…Wavelet transform is an effective mathematic tool that can decompose the original signal into different frequency components and each component is characterized with a resolution appropriate to its decomposion scale [18][19][20]. This method is performed using dilated and shifted versions of the mother wavelet, to produce a set of wavelet basis functions by the following equation:…”

Section: Extraction Of the Wavelet Feature Vectormentioning

confidence: 99%

Estimating Leaf Carotenoid Concentration of Ginger in Different Layers Based on Discrete Wavelet Transform Algorithm

Liao

2019

Computer and Computing Technologies in Agriculture XI

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Ginger is one of the very important industrial crops in southwest, China. Accurate estimation of its leaf carotenoid concentration (LCC) is important to assess ginger photosynthetic capacity and direct the precision agriculture management. This study focused on introducing a new approach for estimating the LCC of ginger leaves in different leave layers. First, five commonly used vegetation indices (PSSR, PSND, CRI550, CRI700, BRI) were performed to estimate the LCC. The PSSR got a better result with the higher estimation accuracy (R 2 =0.46). Second, the discrete wavelet transform algorithm (DWTA) was used to extract the wavelet feature vectors for estimating the LCC. The result showed that the most sensitive wavelet feature vector was in the sixth decomposition scale. The highest estimation accuracy (R 2 ) was 0.86 for the lower leaf layer. Compared with those vegetation indices, the estimation accuracy (R 2 ) improved 46.5%~71.1%, which indicated that the LCC of ginger in different leave layers can be accurately estimated by DWTA.

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“…In recent years, research on image denoising methods has focused on the transform domain. Because wavelet transforms have properties such as multiresolution, low entropy, and decorrelation, so wavelet transforms are often used to process noisy images [6][7][8]. However, the wavelet transform is isotropic, it can only reduce the noise containing horizontal, vertical, and diagonal directions, and the effect of denoising in other directions is not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Morphological Edge Detection Algorithm of Colon Pathological Sections Based on Shearlet

Deng

Wang

et al. 2022

Journal of Mathematics

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This paper proposes an idea of combining the Meyer Shearlet and mathematical morphology to produce the edge detection of pathological sections of the colon. First, the method of constructing a class of sufficiently smooth sigmoid functions along with its relative scale function and Meyer wavelet function is provided in this paper. Based on those, in order to get the new Meyer wavelet function, we use the sigmoid function to construct more general scale functions. Next, taking sufficiently smooth sigmoid functions as examples, combining the relative Meyer wavelet and Shearlet to denoise some pathological sections of the colon leads a decent feedback. At last, this paper provides an improved algorithm for the edge detection of mathematical morphology with the background of multiscale and multistructure. This algorithm is used to carry out the edge detection of images after denoising yields a new edge detection algorithm that fuses the Meyer Shearlet denoising and mathematical morphology. According to the simulation results, the new algorithm is more beneficial for the observation and diagnosis of doctors since the edge noise of the colon pathological image detected by the new algorithm is smaller and provides more continuous and clear lines. Therefore, the fusion algorithm provided in this paper is an effective way to carry out the edge detection of an image.

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Wide-stripe noise removal method of hyperspectral image based on fusion of wavelet transform and local interpolation

Cited by 6 publications

References 15 publications

Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising

Estimating Leaf Carotenoid Concentration of Ginger in Different Layers Based on Discrete Wavelet Transform Algorithm

Morphological Edge Detection Algorithm of Colon Pathological Sections Based on Shearlet

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