Denoising of Remotely Sensed Images Via Curvelet Transform and its Relative Assessment

Raju, C. Nageswara; Reddy, T. Sreenivasulu; Sivasubramanyam, M.

doi:10.1016/j.procs.2016.06.057

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…The curvelet transform has a lower mean square error of the image data with high accuracy and low complexity. It can be used for denoising the image 19,28,29 . Jean‐Luc et al 19 proposes curvelet‐based multiscale denoising using non‐local means and guided image filter.…”

Section: Methodsmentioning

confidence: 99%

“…CLAHE, contrast limited adaptive histogram equalization the image. 19,28,29 Jean-Luc et al 19 proposes curvelet-based multiscale denoising using non-local means and guided image filter. In the two-dimensional (2D) domain ℝ2, all curvelets at scale 2-j are found by rotation and translations of mother curvelet ψ j with different parameters.…”

Section: Denoising Using Curveletmentioning

confidence: 99%

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Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Choudhary

Singhai

Yadav

2021

Int J Imaging Syst Tech

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Effective detection and removal of the artifacts from dermoscopic images require the analysis of structures within lesions for accurate cancer detection. But the dermoscopic images acquired may be affected by image acquisition noise, variation in texture, color, and irrelevant structure such as hairs, air bubbles, and contour blurring around the lesion. Hence preprocessing is very essential for accurate diagnosis. The proposed algorithm includes the preprocessing, to remove acquisition noise using curvelet transform, illumination correction using contrast limited adaptive histogram equalization (CLAHE) algorithm for image enhancement, Frangi vesselness algorithm is utilized for detection and removal of hairs and vessels like structures present in enhanced image, fast marching inpainting method is applied for repairing the information of lesion from removed hair pixels. The performance of the proposed algorithm is analyzed based on commonly used parameters such as diagnostic accuracy (DA), sensitivity (SE), specificity (SP), and precision (P). The performance is evaluated on PH2 and International Symposium on Biomedical Imaging (ISBI) datasets than compared with existing methods. The proposed algorithm segments the skin lesion with the highest diagnostic accuracy of 81.49% and sensitivity of 93.88% for the PH2 dataset and 75.39% and 79.34% respectively for the ISBI dataset. Results are better than existing methods. The proposed algorithm with improved preprocessing for artifact detection and removal is highly accurate and able to retrieve the hair occluded information.

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Section: Methodsmentioning

confidence: 99%

Section: Denoising Using Curveletmentioning

confidence: 99%

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Choudhary

Singhai

Yadav

2021

Int J Imaging Syst Tech

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“…Another deficit of wavelet transformation is the lack of ability to represent edges and geometric structures of the image. Curvelet transform [75][76][77], a multiresolution and multi-direction pyramid that can preserve geometric regularity along edges [78], has been proposed to overcome this difficulty. Ali et al [79] proposed a Curvelet transform (CVT)-based method for the combination of CT and MRI.…”

Section: Wavelet Transformation Based Methodsmentioning

confidence: 99%

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Zhu

2020

Applied Sciences

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With the accelerated development of medical imaging equipment and techniques, image fusion technology has been effectively applied for diagnosis, biopsy and radiofrequency ablation, especially for liver tumor. Tumor treatment relying on a single medical imaging modality might face challenges, due to the deep positioning of the lesions, operation history and the specific background conditions of the liver disease. Image fusion technology has been employed to address these challenges. Using the image fusion technology, one could obtain real-time anatomical imaging superimposed by functional images showing the same plane to facilitate the diagnosis and treatments of liver tumors. This paper presents a review of the key principles of image fusion technology, its application in tumor treatments, particularly in liver tumors, and concludes with a discussion of the limitations and prospects of the image fusion technology.

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“…Among these transforms, the curvelet transform [6] is particularly interesting due to its high spatial and directional selectivity which allows better representation of image features such as edges and contours. Applications of curvelet-based image denoising include seismic exploration [12], remote sensing [16], and astronomical imaging [2].…”

Section: Introductionmentioning

confidence: 99%

Power shrinkage—curvelet domain image denoising using a new scale-dependent shrinkage function

Kadri

Baarir

Schaefer

2019

SIViP

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Image processing and analysis algorithms are at the heart of applications in various scientific fields, such as medical diagnosis, military imaging, and astronomy. However, images are typically exposed to noise contamination during their acquisition and transmission. In this paper, we explore recent advancements in image denoising using curvelet domain shrinkage and present a novel scale dependent shrinkage function, which we call power shrinkage, to enhance restored image quality. Experimental results confirm our proposed method to perform better than classical thresholding and to outperform recent state-of-the-art approaches in denoising different types of noise including speckle, Poisson and additive white Gaussian noise.Keywords image noise • image quality • image denoising • curvelet shrinkage • power shrinkage

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Denoising of Remotely Sensed Images Via Curvelet Transform and its Relative Assessment

Cited by 12 publications

References 13 publications

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Curvelet and fast marching method‐based technique for efficient artifact detection and removal in dermoscopic images

Application of Image Fusion in Diagnosis and Treatment of Liver Cancer

Power shrinkage—curvelet domain image denoising using a new scale-dependent shrinkage function

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