Low Contrast Image Enhancement Using Adaptive Filter and DWT: A Literature Review

Abstract Image enhancement refers to accentuation, or sharpening of image features such as edges, boundaries or contrast to make a graphic display more useful for display and analysis. One of the most common defects of photographic or digital images is poor contrast resulting from a reduced, and perhaps nonlinear, image amplitude range. This paper reviews different algorithms particularly based on adaptive filtering techniques. Weighted filter algorithm, particle swarm optimization(PSO) algorithm, algorithm using hybrid combination of particle filter and wavelet, algorithm using combination of three techniques (median filtering, CLAHE and morphological operation), local tone mapping algorithm and Non-linear adaptive (NLA) algorithm are discussed and compared. This paper concludes about better algorithm which may be the field of research. Keywords: Contrast enhancement; PSO; Adaptive filter; Discrete wavelet transform. 1. Introduction One of the most common defects of photographic or digital images is poor contrast resulting from a reduced, and perhaps nonlinear, image amplitude range 2. Literature review This paper reviews various algorithms based on adaptive filtering techniques for low contrast image enhancement. Weighted filter algorithm In this paper [1] the proposed algorithm uses a weighted filter for enhancing global brightness and contrast of images and wavelet transform to enhance the color information. The flowchart of the proposed method is shown i

Biomedical image denoising based on hybrid optimization algorithm and sequential filters

Background: Nowadays, image de-noising plays a very important role in medical analysis applications and pre-processing step. Many filters were designed for image processing, assuming a specific noise distribution, so the images which are acquired by different medical imaging modalities must be out of the noise. Objectives: This study has focused on the sequence filters which are selected by a hybrid genetic algorithm and particle swarm optimization. Material and Methods: In this analytical study, we have applied the composite of different types of noise such as salt and pepper noise, speckle noise and Gaussian noise to images to make them noisy. The Median, Max and Min filters, Gaussian filter, Average filter, Unsharp filter, Wiener filter, Log filter and Sigma filter, are the nine filters that were used in this study for the denoising of medical images as digital imaging and communications in medicine (DICOM) format. Results: The model has been implemented on medical noisy images and the performances have been determined by the statistical analyses such as peak signal to noise ratio (PSNR), Root Mean Square error (RMSE) and Structural similarity (SSIM) index. The PSNR values were obtained between 59 to 63 and 63 to 65 for MRI and CT images. Also, the RMSE values were obtained between 36 to 47 and 12 to 20 for MRI and CT images. Conclusion: The proposed denoising algorithm showed the significantly increment of visual quality of the images and the statistical assessment. © 2020, Shiraz University of Medical Sciences. All rights reserved

Image Outlier filtering (IOF) : A Machine learning based DWT optimization Approach

In this paper an image outlier technique, which is a hybrid model called SVM regression based DWT optimization have been introduced. Outlier filtering of RGB image is using the DWT model such as Optimal-HAAR wavelet changeover (OHC), which optimized by the Least Square Support Vector Machine (LS-SVM) . The LS-SVM regression predicts hyper coefficients obtained by using QPSO model. The mathematical models are discussed in brief in this paper: (i) OHC which results in better performance and reduces the complexity resulting in (Optimized FHT). (ii) QPSO by replacing the least good particle with the new best obtained particle resulting in 201C;Optimized Least Significant Particle based QPSO201D; (OLSP-QPSO). On comparing the proposed cross model of optimizing DWT by LS-SVM to perform oulier filtering with linear and nonlinear noise removal standards

Partially Linear Estimation with Application to Sparse Signal Recovery From Measurement Pairs

We address the problem of estimating a random vector X from two sets of measurements Y and Z, such that the estimator is linear in Y. We show that the partially linear minimum mean squared error (PLMMSE) estimator does not require knowing the joint distribution of X and Y in full, but rather only its second-order moments. This renders it of potential interest in various applications. We further show that the PLMMSE method is minimax-optimal among all estimators that solely depend on the second-order statistics of X and Y. We demonstrate our approach in the context of recovering a signal, which is sparse in a unitary dictionary, from noisy observations of it and of a filtered version of it. We show that in this setting PLMMSE estimation has a clear computational advantage, while its performance is comparable to state-of-the-art algorithms. We apply our approach both in static and dynamic estimation applications. In the former category, we treat the problem of image enhancement from blurred/noisy image pairs, where we show that PLMMSE estimation performs only slightly worse than state-of-the art algorithms, while running an order of magnitude faster. In the dynamic setting, we provide a recursive implementation of the estimator and demonstrate its utility in the context of tracking maneuvering targets from position and acceleration measurements.Comment: 13 pages, 5 figure

Video modeling via implicit motion representations

Video modeling refers to the development of analytical representations for explaining the intensity distribution in video signals. Based on the analytical representation, we can develop algorithms for accomplishing particular video-related tasks. Therefore video modeling provides us a foundation to bridge video data and related-tasks. Although there are many video models proposed in the past decades, the rise of new applications calls for more efficient and accurate video modeling approaches.;Most existing video modeling approaches are based on explicit motion representations, where motion information is explicitly expressed by correspondence-based representations (i.e., motion velocity or displacement). Although it is conceptually simple, the limitations of those representations and the suboptimum of motion estimation techniques can degrade such video modeling approaches, especially for handling complex motion or non-ideal observation video data. In this thesis, we propose to investigate video modeling without explicit motion representation. Motion information is implicitly embedded into the spatio-temporal dependency among pixels or patches instead of being explicitly described by motion vectors.;Firstly, we propose a parametric model based on a spatio-temporal adaptive localized learning (STALL). We formulate video modeling as a linear regression problem, in which motion information is embedded within the regression coefficients. The coefficients are adaptively learned within a local space-time window based on LMMSE criterion. Incorporating a spatio-temporal resampling and a Bayesian fusion scheme, we can enhance the modeling capability of STALL on more general videos. Under the framework of STALL, we can develop video processing algorithms for a variety of applications by adjusting model parameters (i.e., the size and topology of model support and training window). We apply STALL on three video processing problems. The simulation results show that motion information can be efficiently exploited by our implicit motion representation and the resampling and fusion do help to enhance the modeling capability of STALL.;Secondly, we propose a nonparametric video modeling approach, which is not dependent on explicit motion estimation. Assuming the video sequence is composed of many overlapping space-time patches, we propose to embed motion-related information into the relationships among video patches and develop a generic sparsity-based prior for typical video sequences. First, we extend block matching to more general kNN-based patch clustering, which provides an implicit and distributed representation for motion information. We propose to enforce the sparsity constraint on a higher-dimensional data array signal, which is generated by packing the patches in the similar patch set. Then we solve the inference problem by updating the kNN array and the wanted signal iteratively. Finally, we present a Bayesian fusion approach to fuse multiple-hypothesis inferences. Simulation results in video error concealment, denoising, and deartifacting are reported to demonstrate its modeling capability.;Finally, we summarize the proposed two video modeling approaches. We also point out the perspectives of implicit motion representations in applications ranging from low to high level problems

Statistical analysis and modeling for biomolecular structures

Most of the recent studies on biomolecules address their three dimensional structure since it is closely related to their functions in a biological system. Determination of structure of biomolecules can be done by using various methods, which rely on data from various experimental instruments or on computational approaches to previously obtained data or datasets. Single particle reconstruction using electron microscopic images of macromolecules has proven resource-wise to be useful and affordable for determining their molecular structure in increasing details. The main goal of this thesis is to contribute to the single particle reconstruction methodology, by adding a process of denoising in the analysis of the cryo-electron microscopic images. First, the denoising methods are briefly surveyed and their efficiencies for filtering cryo-electron microscopic images are evaluated. In this thesis, the focus has been set to information theoretic minimum description length (MDL) principle for coding efficiently the essential part of the signal. This approach can also be applied to reduce noise in signals and here it is used to develop a novel denoising method for cryo-electron microscopic images. An existing denoising method has been modified to suit the given problem in single particle reconstruction. In addition, a more general denoising method has been developed, discovering a novel way to find model class by using the MDL principle. This method was then thoroughly tested and compared with co-existing methods in order to evaluate the utility of denoising in single particle reconstruction. A secondary goal in the research for this thesis deals with studying protein oligomerisation, using computational approaches. The focus has been to recognize interacting residues in proteins for oligomerization and to model the interaction site for hantavirus N-protein. In order to unravel the interaction structure, the approach has been to understand the phenomenon of protein folding towards quaternary structure.reviewe

Feature-preserving image restoration and its application in biological fluorescence microscopy

This thesis presents a new investigation of image restoration and its application to fluorescence cell microscopy. The first part of the work is to develop advanced image denoising algorithms to restore images from noisy observations by using a novel featurepreserving diffusion approach. I have applied these algorithms to different types of images, including biometric, biological and natural images, and demonstrated their superior performance for noise removal and feature preservation, compared to several state of the art methods. In the second part of my work, I explore a novel, simple and inexpensive super-resolution restoration method for quantitative microscopy in cell biology. In this method, a super-resolution image is restored, through an inverse process, by using multiple diffraction-limited (low) resolution observations, which are acquired from conventional microscopes whilst translating the sample parallel to the image plane, so referred to as translation microscopy (TRAM). A key to this new development is the integration of a robust feature detector, developed in the first part, to the inverse process to restore high resolution images well above the diffraction limit in the presence of strong noise. TRAM is a post-image acquisition computational method and can be implemented with any microscope. Experiments show a nearly 7-fold increase in lateral spatial resolution in noisy biological environments, delivering multi-colour image resolution of ~30 nm