Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Routray, Sidheswar; Ray, Arun Kumar; Mishra, Chandrabhanu

doi:10.1016/j.ijleo.2018.01.096

Cited by 25 publications

(7 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

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

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

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

View full text Add to dashboard Cite

show abstract

“…5. Experiment of catenary image enhancement (1) In Fig. 5, the contrast of the original image is low.…”

Section: Block Diagram Of Image Enhancementmentioning

confidence: 99%

“…This makes it more effective in processing image edges, texture and other geometric features than conventional algorithms. As for now, Curvelet transform has been used in many fields such as denoising [1,2], detection [3,4], recognition [5,6], diagnosis [7,8], enhancement [9], compression [10], watermarking [11], estimation [12], steganography [13], segmentation [9,14], classification [9, 15,1 6], etc. Moreover, many combination methods based on Curvelet transform and other methods have been proposed in many areas.…”

mentioning

confidence: 99%

Catenary image enhancement method based on Curvelet transform with adaptive enhancement function

Wu¹

2019

Diagnostyka

View full text Add to dashboard Cite

In the process of catenary failure diagnosis system based on image processing technique, some catenary images present low contrast, which need to be enhanced. Curvelet transform has the high directional sensitivity and anisotropy, which is suitable for image enhancement because of its optimal sparse representation of image with rich details and edges. First, the catenary image is decomposed by Curvelet transform to get its high and low frequency coefficients, then adjust the high frequency coefficients using the enhancement function. Afterwards, combine the high frequency coefficients and low frequency coefficients by the inverse Curvelet transform, and thus to get the enhanced catenary image. In this paper, Curvelet transform is compared with the traditional enhancement methods. The experimental results show that the proposed method can effectively enhance the low contrast catenary images, the catenary insulator, arm, hanger, pillar and locator part become visible, the details become more obvious. Moreover, as for the online application of catenary failure diagnosis system, efficiency is another important consideration. The experimental results also show that the cost time of catenary image enhancement is within a few tens of seconds, which meets the requirements of catenary failure diagnosis system.

show abstract

“…Weighted bilateral filter through Curvlet transform [6] provided insight into not only providing the user with image restoration but also an understanding of the image. Total variation minimisation approach was presented in [7] to denoise and deblur the Gold Immuno Chromatographic Strip images.…”

Section: Related Workmentioning

confidence: 99%

Linearly uncorrelated principal component and deep convolutional image deblurring for natural images

Jeyaprakash¹,

Sudhakar²

2019

IET Image Processing

View full text Add to dashboard Cite

Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform

Cited by 25 publications

References 29 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

Catenary image enhancement method based on Curvelet transform with adaptive enhancement function

Linearly uncorrelated principal component and deep convolutional image deblurring for natural images

Contact Info

Product

Resources

About