Image Denoising Method based on Curvelet Transform with Thresholding Functions (original) (raw)

Image Denoising Based on Curvelet Transforms and its Comparative Study with Basic Filters

2013

Image denoising is basic work for image processing, analysis and computer vision. This Work proposes a Curvelet Transformation based image denoising, which is combined with the low pass filtering and thresholding methods in the transform domain. Through simulations with images contaminated by white Gaussian noise, this scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect as compared to basic filters. Curvelet transformation is a multi-scale transformation technique which is most suitable for the objects with curves. Introduction Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising ago...

Image denoising using curvelet transform: an approach for edge preservation

Journal of Scientific & Industrial Research, 2010

This paper suggests a soft thresholding multiresolution technique based on local variance estimation for image denoising. This adaptive thresholding with local variance estimation effectively reduce image noise and preserves edges. In proposed algorithm, 2D fast discrete curvelet transform (2D FDCT) out performed wavelet based image denoising. PSNR using 2D FDCT is approximately doubled and it also preserves features at boundary of an image.

Modified Curvelet Thresholding Algorithm for Image Denoising

Journal of Computer Science

Problem statement: This study introduced an adaptive thresholding method for removing additive white Gaussian noise from digital images. Approach: Curvelet transform employed in the proposed scheme provides sparse decomposition as compared to the wavelet transform methods which being nongeometrical lack sparsity and fail to show optimal rate of convergence. Results: Different behaviors of curvelet transform maxima of image and noise across different scales allow us to design the threshold operator adaptively. Multiple thresholds depending on the scale and noise variance are calculated to locally suppress the curvelet transform coefficients so that the level of threshold is different at every scale. Conclusion/Recommendations: The proposed algorithm succeeded in providing improved denoising performance to recover the shape of edges and important detailed components. Simulation results proved that the proposed method can obtain a better image estimate than the wavelet based restoration methods.

A 4-quadrant Curvelet Transform for Denoising Digital Images

International Journal of Automation and Computing, 2013

The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.

PERFORMANCE ANALYSIS OF COLOR IMAGE DENOISING USING CURVELET TRANSFORM BASED TECHNIQUE

In this paper we propose a new method to reduce noise in color image. The images corrupted by Gaussian Noise is still a classical problem. To reduce the noise or to improve the quality of image we have used different parameters. The proposed method succeeded in providing improved image denoising performance to recover the shape of edges and important detailed components. The experimental results proved that the proposed technique can obtain a better image estimate than the curvelet transform based restoration methods.

MEDICAL IMAGE DENOISE METHOD BASED ON CURVELET TRANSFORM: AN APPROACH FOR EDGE PRESERVATION

In medical images noise and artifacts are presented due to the measurement techniques and instrumentation. Because of the noise present in the medical images, physicians are unable to obtain required information from the images. The paper proposes a noise reduction method for both computed tomography (CT) and magnetic resonance imaging (MRI) which fuses the Curvelet transform based method. The performance is analysed by computing Peak Signal to Noise Ratio (PSNR).The results show the proposed method can obtain enhanced visual and de-noise effect.

Segmentation Based Combined Wavelet-Curvelet Approach for Image Denoising

International Journal of Information Engineering

This paper presents an efficient image denoising method that adaptively combines the features of wavelets, wave atoms and curvelets. Wavelet shrinkage is used to denoise the smooth regions in the image while wave atoms are employed to denoise the textures, and the edges will take advantage of curvelet denoising. The received noisy image is firstly decomposed into a homogenous (smooth/cartoon) part and a textural part. The cartoon part of the noisy image is denoised using wavelet transform, and the texture part of the noisy image is denoised using wave atoms. The two denoised images are then fused adaptively. For adaptive fusion, different weights are chosen from the variance map of the denoised texture image. Further improvement in denoising results is achieved by denoising the edges through curvelet transform. The information about edge location is gathered from the variance map of denoised cartoon image. The denoised image results in perfect presentation of the smooth regions and efficient preservation of textures and edges in the image.

Medical Image Denoising using Fast Discrete Curvelet Transform

International Journal of Advanced Trends in Computer Science and Engineering, 2020

Now-a-days, medical field plays a very crucial role in our daily life, as a part of it MRI (Magnetic resonance imaging) scans, CT (computed tomography) images, Ultrasound images etc. of the victim which are one of the main things that are to be determined correctly based on which the patient's condition is concluded and treated. The main problem here occurs is for the original image where the image gets noisy and the features of the original image are lost due to many factors. So, here in our paper, we instigate the method of image denoising technique which helps to eliminate the noisy observations and other disturbances and reconstructs the original image very accurately. The image denoising is one of the important preprocessing steps in medical field image processing analysis. For this denoising method, we are going to use the Fast Discrete Curvelet Transform which is a multi-scale geometric transform and is designed to signify the image or video sequences at different scales and angles. also the performances of it by using fast Fourier discrete curve-let transform which is based on ridge-let analysis theory for denoising procedures and makes recommendations with the help of adaptive threshold algorithm which is applied on the image and gets the original image with effectiveness also retrieves the important detail features in the image and also the quality of the image to be recovered by using the parameters like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE).

Noise Removal Technique Using Curvelet Transform and Filtering approach of Satellite Images

Noise removal from Satellite Images is a major area of research and there are many techniques used for this process. Due to the presence of noise the images are corrupted and the information present in the image is not clearly visible.Many transform techniques and filters were used for removal of noise and enhancement of images.In this paper we use Curvelet transform which is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of the object with edges. Curvelet transform has the advantage of handling curve discontinuities very well. Curvelet transform is used in this paper to remove the noise present in the image and also to improve the edges. Filters like Gabor filter and Unsharp filter is also used along with curvelet to remove the noise and to increase the sharpness of the image. Quantitative parameters like PSNR,Entropy is used to measure the quality of the image.The results shows the superiority of the proposed method.

IRJET- ANALYSIS OF WAVELET, RIDGELET AND CURVELET TRANSFORMS ON IMAGE DENOISING

IRJET, 2021

Image processing refers to digital image processing to extract more information than is shown in the original image. It is an integral part of the applications used in publishing, satellite imagery analysis, medical fields, earthquakes, and many different fields. Images are often problematic with high-level components of noises. Image denoising is the technique of removing various types of noises or distortions from the corrupted image while retaining the edges and other features as detailed as possible. To achieve this goal, it makes use of a mathematical function known as the wavelet transform. Wavelet transform has unique advantages when it comes to working with a single point, but has some disadvantages when it comes to working with the borders and the line features of an image. There was also a Ridgelet transform that could only represent images of line singularities in two-dimensional space and not the curve singularity. For better representations of images with curve singularities in high dimensions, a Curvelet transform was introduced. An image's edges can be represented in a very useful way with the Curvelet transform since it is anisotropic with strong directional characteristics. The objective of this research is to analyze the wavelet, ridgelet, and curvelet transform for removing multiple types of noise from an image and determining the most appropriate transform among them. The wavelet, ridgelet, and curvelet transform for image denoising are implemented using Matlab. We analyze the accuracy, scalability, and graphical representation of image denoising based on wavelets, Ridgelets, and Curvelets.