Removal Of Blocking Artifacts From JPEG-Compressed Images Using An Adaptive Filtering Algorithm

The aim of this research was to develop an algorithm that will produce a considerable improvement in the quality of JPEG images, by removing blocking and ringing artifacts, irrespective of the level of compression present in the image. We review multiple published related works, and finally present a computationally efficient algorithm for reducing the blocky and Gibbs oscillation artifacts commonly present in JPEG compressed images. The algorithm alpha-blends a smoothed version of the image with the original image; however, the blending is controlled by a limit factor that considers the amount of compression present and any local edge information derived from the application of a Prewitt filter. In addition, the actual value of the blending coefficient (α) is derived from the local Mean Structural Similarity Index Measure (MSSIM) which is also adjusted by a factor that also considers the amount of compression present. We also present our results as well as the results for a variety of other papers whose authors used other post compression filtering methods

Removal Of Blocking Artifacts From JPEG-Compressed Images Using Neural Network

The goal of this research was to develop a neural network that will produce considerable improvement in the quality of JPEG compressed images, irrespective of compression level present in the images. In order to develop a computationally efficient algorithm for reducing blocky and Gibbs oscillation artifacts from JPEG compressed images, we integrated artificial intelligence to remove blocky and Gibbs oscillation artifacts. In this approach, alpha blend filter [7] was used to post process JPEG compressed images to reduce noise and artifacts without losing image details. Here alpha blending was controlled by a limit factor that considers the amount of compression present, and any local information derived from Prewitt filter application in the input JPEG image. The outcome of modified alpha blend was improved by a trained neural network and compared with various other published works [7][9][11][14][20][23][30][32][33][35][37] where authors used post compression filtering methods

Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering

Perivascular spaces (PVSs) in brain have a close relationship with typical neurological diseases. The quantitative studies of PVSs are meaningful but usually difficult, due to their thin and weak signals and also background noise in the 7 T brain magnetic resonance images (MRI). To clearly distinguish the PVSs in the 7 T MRI, we propose a novel PVS enhancement method based on the Haar transform of non-local cubes. Specifically, we extract a certain number of cubes from a small neighbor to form a cube group, and then perform Haar transform on each cube group. The Haar transform coefficients are processed using a nonlinear function to amplify the weak signals relevant to the PVSs and to suppress the noise. The enhanced image is reconstructed using the inverse Haar transform of the processed coefficients. Finally, we perform a block-matching 4D filtering on the enhanced image to further remove any remaining noise, and thus obtain an enhanced and denoised 7 T MRI for PVS segmentation. We apply two existing methods to complete PVS segmentation, i.e., (1) vesselness-thresholding and (2) random forest classification. The experimental results show that the PVS segmentation performances can be significantly improved by using the enhanced and denoised 7 T MRI

An efficient computational scheme for the two-dimensional overcomplete wavelet transform

2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Laser-Directed Self-Organization and Reaction Control in Complex Systems

Pulsed lasers proved to be advantageous tools for the stimulation of pattern formation in complex systems. Their capability to support thermodynamic, kinetic and spatial control facilitates the direction of self-organization processes into selective channels. The short lifetime of laser-stimulated processes was identified to be the key aspect that enables for the synthesis of functional materials starting from complex systems. When self-organization is abruptly stopped after a few nanoseconds, this creates materials present in a non-equilibrium state, which are known to exhibit special properties. A prominent example is the distinctively different behavior of gold nanoparticles compared to bulk gold. Repeated laser stimulation was demonstrated to be a powerful method that enables selective adjustments of material properties emergent in the course of self-organized pattern formation in complex systems. This includes a broad spectrum of optical, electrical, magnetic and catalytic properties, which are not found in the starting materials prior to laser modification. The capability of lasers to trigger self-organization processes with spatial control was identified to be an interesting feature because it bears the potential to create materials with advanced functionality. In particular, the utilization of a phenomenon called laser-induced periodic surface structures (LIPSS) proved to be very efficient. LIPSS transformed the surface of stainless steel into hierarchical structures thus equipping this everyday material with a multifunctional surface. Considering the simplicity of the generation process this demonstrates the viability of nature’s low-effort-high-outcome-principle of order formation in complex systems. In addition to that, the application breath of laser-stimulated pattern formation was successfully expanded to temperature sensitive materials by including photochemistry into the concept. The large variety of reaction types accessible via photochemistry opens an even wider field of potential applications. In conclusion, it can be stated that the concept of nature to trigger selective reorganizations and pattern formation in complex systems can be imitated in its principles. The introduced concept of laser-directed self-organization and reaction control in complex systems prospects a large application potential. Presented insights into laser-stimulated reaction pathways and pattern formations processes provide a valuable basis for future studies in this field. Overall, the major challenge that must be met on the way to beneficial applications is the need for purposeful design of materials, which requires a thorough understanding of the fundamental principles behind self-organization

Sixth NASTRAN (R) Users' Colloquium

Papers are presented on NASTRAN programming, and substructuring methods, as well as on fluids and thermal applications. Specific applications and capabilities of NASTRAN were also delineated along with general auxiliary programs

Multiscale Hybrid Nonlocal Means Filtering Using Modified Similarity Measure

A new multiscale implementation of nonlocal means filtering (MHNLM) for image denoising is proposed. The proposed algorithm also introduces a modification of the similarity measure for patch comparison. Assuming the patch as an oriented surface, the notion of a normal vectors patch is introduced. The inner product of these normal vectors patches is defined and then used in the weighted Euclidean distance of intensity patches as the weight factor. The algorithm involves two steps: the first step is a multiscale implementation of an accelerated nonlocal means filtering in the discrete stationary wavelet domain to obtain a refined version of the noisy patches for later comparison. The next step is to apply the proposed modification of standard nonlocal means filtering to the noisy image using the reference patches obtained in the first step. These refined patches contain less noise, and consequently the computation of normal vectors and partial derivatives is more precise. Experimental results show equivalent or better performance of the proposed algorithm compared to various state-of-the-art algorithms

Wavelet-Based Registration of Medical Images.

Registration is the process of spatially aligning two objects and is normally a preprocessing step in most object recognition algorithms. Registration of images and recognition of signatures of objects in images is important for clinical and diagnostic purposes in medicine. Recognizing structure, potential targets for defense purposes and changes in the terrain, from aerial surveillance images and SAR images is the focus of extensive research and development today. Automatic Target Recognition is becoming increasingly important as the defense systems and armament technology move to use smarter munitions. Registration of images is a preprocessing step in any kind of machine vision for robots, object recognition in general, etc. Registration is also important for tuning instruments dealing with images. Most of the available methods of registration today are operator assisted. The state of registration today is more art than science and there are no standards for measuring or validating registration procedures. This dissertation provides a viable method to automatically register images of rigid bodies. It provides a method to register CT and MRI images of the brain. It uses wavelets to determine sharp edges. Wavelets are oscillatory functions with compact support. The Wavelet Modulus Maxima singularides. It also provides a mechanism to characterize the singularities in the images using Lipschitz exponents. This research provides a procedure to register images which is computationally efficient. The algorithms and techniques are general enough to be applicable to other application domains. The discussion in this dissertation includes an introduction to wavelets and time frequency analysis, results on MRI data, a discussion on the limitations, and certain requirements for the procedure to work. This dissertation also tracks the movement of edges across scales when a wavelet algorithm is used and provides a formula for this edge movement. As part of this research a registration classification schematic was developed

Real-time noise-aware tone mapping

Real-time high quality video tone mapping is needed for many applications, such as digital viewfinders in cameras, display algorithms which adapt to ambient light, in-camera processing, rendering engines for video games and video post-processing. We propose a viable solution for these applications by designing a video tone-mapping operator that controls the visibility of the noise, adapts to display and viewing environment, minimizes contrast distortions, preserves or enhances image details, and can be run in real-time on an incoming sequence without any preprocessing. To our knowledge, no existing solution offers all these features. Our novel contributions are: a fast procedure for computing local display-adaptive tone-curves which minimize contrast distortions, a fast method for detail enhancement free from ringing artifacts, and an integrated video tone-mapping solution combining all the above features.This project was funded by the Swedish Foundation for Strategic Research (SSF) through grant IIS11-0081, Linkoping University Center for Industrial Information Technology (CENIIT), the Swedish Research Council through the Linnaeus Environment CADICS, and through COST Action IC1005