Denoising Various Imaging Modalities using Wavelets, Ridgelets and Curvelets: A Comparative Analysis

Multi-modal image acquisition techniques have allowed digital images to penetrate domains from micro-scale medical imaging to mega-scale satellite imaging. For postprocessing, deep learning techniques have widely been used for image denoising and artifact suppression. However, far little work has been done to summarize their effectiveness concerning adaptive filter design, e.g., salt and pepper noise, stochastic Poisson, or additive white noise. Because different images, natural or urban, structured or unstructured scenes, and objects produce different types of noise, from the modality as well as from the imaging medium, devising a single method for all noise types is impractical. This paper proposes to use reinforcement learning (Q-learning) to adaptively design filters of a convolutional neural network (CNN). In contrast to the popular state of the art methods that use filter designs based on the noise model, CNN filters lack the power to do so. We have attempted to address this limitation of CNN by introducing a new modality of reinforcement learning for adaptive filter design. The qualitative and quantitative analysis of the proposed method is done and its efficacy is demonstrated using the following evaluation metrics: Peak Signal to Noise Ratio (PSNR), Contrast to Noise Ratio (CNR), and Structure Similarity Index (SSIM).

Image process is one among most growing analysis space these days and currently it's greatly integrated with the medical and biotechnology field. Image process will be used to analyze different medical and MRI pictures to induce the abnormality within the image. In medical image processing, medical images are corrupted by different type of noises. It is very important to obtain precise images to facilitate accurate observations for the given application. Removing of noise from medical images is now a very challenging issue in the field of medical image processing. Most well known noise reduction methods, which are usually based on the local statistics of a medical image, are not efficient for medical image noise reduction. This paper presents an efficient and simple method for noise reduction from medical images and experimental results are also compared with the other three image filtering algorithms. The quality of the output images is measured by the statistical quantity measures: peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and root mean square error (RMSE) proposes associate economical K-means clump algorithm underneath Morphological Image process (MIP). Medical Image segmentation deals with segmentation of tumour in CT and MR pictures for improved quality in diagnosis. It is an important method and a difficult drawback because of noise presence in input pictures throughout image analysis. It's required for applications involving estimation of the boundary of associate object, classification of tissue abnormalities, form analysis, contour detection. Segmentation determines because the method of dividing associate image into disjoint consistent regions of a medical image. The amount of resources needed to explain massive set of information is simplified and is chosen for tissue segmentation. In our paper, this segmentation is administrated victimization K-means clump algorithm for higher performance. This enhances the growth boundaries more and is extremely fast in comparison to several other clustering algorithms. This paper produces the reliable results that area unit less sensitive to error. Keywords: Magnetic resonance image, Morphological Image processing, Image segmentation, K-means, Morphological operations, Fuzzy, PSNR, RMSE.

An efficient filter must have the quality of removing noise at the same time preserving the information. On the other hand, noise contaminates in the field of signal processing and communication due to various unwanted nonlinear effects. So study of the characteristics of different types of noise is necessary. Also, how various types of filters response to these noises are also important? In this paper, we have studied the characteristics of four types of noise namely Salt & pepper noise, Gaussian noise, Speckle noise and Poisson noise and analyzed the performance of four basic types of filters such as Mean filter, Median filter, Wiener filter and Gaussian filter to suppress the noises mentioned previously.

Automatic image segmentation turns severely crucial for the detection of tumor in the processing of medical image. Semi-automatic and manual techniques of segmentation require immense knowledge and time. These drawbacks however have been overcome by the hybrid segmentation, yet there are requirements to develop many techniques that are appropriate for the medical image segmentation. Hence, we generated image segmentation based on hybrid approach making use of the features of threshold segmentation technique and region growing techniques combined. This is followed by the pre-processing stage to give away an accurate extraction of brain tumor with the help of (MRI) magnetic resonance imaging. The threshold segmentation method is made use of for the segmentation. Four types of noise are undergone in the segmented image: Gaussian noise, salt and pepper noise, Poisson noise and speckle noise which are influenced in brain smear image and they are removed using the filters of four types: median filter, mean filter, wiener filter and Gaussian filter to test the efficiency of the variety of filters over the various kinds of noise. To estimate the parametric values, we can make use of PSNR, NAE, MSE and NK.

Noise is one of the prime factors which degrade the quality of an image. Hence, image denoising is an essential image enhancement technique in the image processing domain. In this paper, we use low-pass sparse banded filter matrices for image denoising. Sparsity is the key concept in this filter design. We applied the designed low-pass filter both row-wise and column-wise to denoise the image. The proposed method is experimented on standard test images corrupted with different types of noises namely Gaussian, White Gaussian, Salt & Pepper and Speckle with noise level equals to 0.01, 0.05 and 0.1. The effectiveness of the proposed method of denoising is evaluated by the computation of standard quality metric known as Peak Signal-to-Noise Ratio (PSNR). The experimental result analysis shows that the proposed image denoising technique based on sparse banded filter matrices results in significant improvement in PSNR around 2dB to 8dB for different type of noises with noise level equal to 0.1 and is also aided by the visual analysis.

Lung Cancer in today’s world is one of the major widespread dangerous diseases which is the subject of maximum deaths every year. Accurate detection of lung cancer could boost the endurance rate. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. Images from a CT scan are widely used because they provide comprehensive imaging of tumor progression inside the lungs. Although different types of noise might be experienced while doing CT scans, producing it a monotonous task for recognizing tumors in the lung. Elimination of noise in CT images is a challenging task for medical diagnoses. The presence of noise in an image is inevitable. Hence reducing noise from the CT scan image is critical for further analysis. Hence various filtering techniques have been used that denoise and enhance the image and help in further evaluation of CT images for accurate lung cancer detection. This paper analyses the noises of different kinds in the CT images and different noise removal techniques which help in improving the accuracy of segmentation and feature extraction as they remove unwanted noise and contribute to the accurate detection of lung cancer. The various filtering methods are analyzed with salt along with pepper noise and speckle noise. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. The experimental results show that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer MSE values of 214.8522, high SNR value of 19.36304, SSIM value 0.595997, and high PSNR value of 24.80941.

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

Most of the medical images are usually affected by different types of noises during acquisition, storage, and transmission. These images need to be free from noise for better diagnosis, decision, and results. Thus, denoising technique plays an important role in medical image analysis. This paper presents a method of noise removal for brain magnetic resonance imaging (MRI) image using curvelet transform thresholding technique combined with the Wiener filter and compares the result with the curvelet and wavelet-based denoising techniques. To assess the quality of denoised image, the values of peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index measure (SSIM) are considered. The experimental results show that curvelet denoising method depicts better result than wavelet denoising method, but the combined method of curvelet with Wiener filtering technique is more effective than the wavelet- and curvelet-based denoising method in terms of PSNR, MSE, and SSIM.

In recent years most of researcher’s has done tremendous work in the field of medical image applications such as Magnetic Resonance Imaging (MRI), Ultra Sound, CT scan but still there are many research and experiments in medical imaging field and diagnosing of human health by Health Care Institutes. There is a growing interest for medical imaging de-noising as a hot area of research and also imaging equipment as a device. It is used for better image processing and highlighting the important features. These images are affected with random noise during acquisition, analyzing and Transmission process. This results in blurry image visible in low contrast. Wavelet transforms have effective method to separate the noise from the original medical image by using threshold techniques without affecting the important data of an image. Wavelet transform enables us to use the forward wavelet transform to represent sub-band of the original image in decomposition process then reconstructing this sub band coefficients to original image using inverse wavelet transform. In this work, the quality of medical image has been evaluated using filter assessment parameters like Variance, standard deviation, the squared difference error between original medical image & de-noised image (MSE) and the ratio between original image & noisy image. From numerical results, we can see that the algorithm is efficient de-noising of noisy medical image. When, investigating with Baye’s threshold techniques it achieved the Best value of peak signal to noise ratio (PSNR). For best medical image de-noising, the wavelet based de-noising algorithm has been investigated and results of Baye’s techniques and hard & soft threshold methods have been compared.

TEM (Transmission Electron Microscopy) is an important morphological characterization tool for Nanomaterials. Quite often a microscopy image gets corrupted by noise, which may arise in the process of acquiring the image, or during its transmission, or even during reproduction of the image. Removal of noise from an image is one of the most important tasks in image processing. Denoising techniques aim at reducing the statistical perturbations and recovering as well as possible the true underlying signal. Depending on the nature of the noise, such as additive or multiplicative type of noise, there are several approaches towards removing noise from an image. Image De-noising improves the quality of images acquired by optical, electro-optical or electronic microscopy. This paper compares five filters on the measures of mean of image, signal to noise ratio, peak signal to noise ratio & mean square error. In this paper four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, DVROFT). Further results have been compared for all noises. It is observed that for Gaussian Noise WFDWT & for other noises HMDF has shown the better performance results.

Objectives: Image Denoising refers to the recovery of an image that has been corrupted by noise due to poor quality of image acquisition and transmission. Accordingly, there is a need to reduce the noise present in the image as a consequence of the denoised image formed. Methods/Statistical Analysis: This paper presents Image denoising using Wavelet transforms, Contourlet transforms and Block Matching Transforms governed by bivariate shrinkage (Bi-shrink) filter techniques. The Wavelet transform uses up-sampling, down-sampling, low pass filter and high pass filter to perform denoising operation, the Contourlet transform uses up-sampling, down-sampling, low pass filter and high pass filter and directional filter banks to perform denoising operation, the Block Matching Transform uses Haar Transforms, Discrete cosine transforms and Karhunen Loeve transform to perform denoising operation. Findings: The performance of wavelet transforms, Contourlet transforms and Block Matching Transforms are evaluated for Reference images (such as towers, shades and ruler images) corrupted by gaussian noise and salt and pepper noise, by computing two error metrics Peak Signal to Noise Ratio (PSNR) and Execution Time (ET) with help of shrinkage function. Programming these using MATLAB R2014a by exploring its wavelet transform, Contourlet transform, image processing and signal processing toolboxes and the values are presented in tabular forms and discussed in the section 6. In this paper the block matching haar discrete cosine transform is proposed for denoising of images (especially for those images possessing detailed textures) that works through haar transform and discrete cosine transform outstrips the basic transform discrete wavelet transform and semi translation invariant contourlet transform. For the images corrupted by Gaussian noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 6.71 dB, ET = 25.89 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 5.49 dB, ET = 5.89 sec”. For the images corrupted by Salt and Pepper noise and denoised by the proposed transform outstrips the basic transform “Discrete Wavelet Transform by PSNR = 21.15 dB, ET = 0.27 sec” and “Semi Translation Invariant Contourlet Transforms by PSNR = 20.05 dB, ET = 5.80 sec”. Application/Improvements: In this paper Block Matching Haar Discrete cosine transform is proposed to overcome the limitations of wavelet transforms and Contourlet transforms, hence to attain the trade-off between high peak signal to noise ratio and less execution time. Results and Discussion section illustrates the efficacy of the proposed transform in terms of peak signal to noise ratio, execution time and visual quality of images.

Medical ultrasound imaging has transformed the disease identification in the human body in the last few decades. The major setback for ultrasound medical images is speckle noise. Speckle noise is created in ultrasound images due to numerous reflections of ultrasound signals from hard tissues of human body. Speckle noise corrupts the medical ultrasound images dropping the detectable quality of the image. An endeavor is made to recover the image quality of ultrasound medical images by using block based hard and soft thresholding of wavelet coefficients. Medical ultrasound image is transformed to wavelet domain using debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients are applied. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments were conducted on medical ultrasound images obtained from diagnostic centers in Vijayawada, India. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSEVI).

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.

The ever-growing numbers of medical digital images and the need to share them among specialists and hospitals for better and more accurate diagnosis require that patients' privacy be protected. During the transmission of medical images between hospitals or specialists through the network, the main priority is to protect a patient's documents against any act of tampering by unauthorised individuals. Because of this, there is a need for medical image authentication scheme to enable proper diagnosis on patient. In addition, medical images are also susceptible to salt and pepper impulse noise through the transmission in communication channels. This noise may also be intentionally used by the invaders to corrupt the embedded watermarks inside the medical images. A common drawback of existing watermarking methods is their weakness against salt and pepper noise. The research carried out in this work addresses the issue of designing a new watermarking method that can withstand high density of salt and pepper noise for brain MRI images. For this purpose, combination of a spatial domain watermarking method, channel coding and noise filtering schemes are used. The region of non-interest (RONI) of MRI images from five different databases are used as embedding area and electronic patient record (EPR) is considered as embedded data. The quality of watermarked image is evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the accuracy of the extracted watermark is assessed in terms of Bit Error Rate (BER).

Image denoising and quality measurements by using filtering and wavelet based techniques