Adaptive Edge-guided Block-matching and 3D filtering (BM3D) Image Denoising Algorithm

Image denoising is a well studied field, yet reducing noise from images is still a valid challenge. Recently proposed Block-matching and 3D filtering (BM3D) is the current state of the art algorithm for denoising images corrupted by Additive White Gaussian noise (AWGN). Though BM3D outperforms all existing methods for AWGN denoising, still its performance decreases as the noise level increases in images, since it is harder to find proper match for reference blocks in the presence of highly corrupted pixel values. It also blurs sharp edges and textures. To overcome these problems we proposed an edge guided BM3D with selective pixel restoration. For higher noise levels it is possible to detect noisy pixels form its neighborhoods gray level statistics. We exploited this property to reduce noise as much as possible by applying a pre-filter. We also introduced an edge guided pixel restoration process in the hard-thresholding step of BM3D to restore the sharpness of edges and textures. Experimental results confirm that our proposed method is competitive and outperforms the state of the art BM3D in all considered subjective and objective quality measurements, particularly in preserving edges, textures and image contrast

A Contrast- and Luminance-Driven Multiscale Netowrk Model of Brightness Perception

A neural network model of brightness perception is developed to account for a wide variety of data, including the classical phenomenon of Mach bands, low- and high-contrast missing fundamental, luminance staircases, and non-linear contrast effects associated with sinusoidal waveforms. The model builds upon previous work on filling-in models that produce brightness profiles through the interaction of boundary and feature signals. Boundary computations that are sensitive to luminance steps and to continuous lumi- nance gradients are presented. A new interpretation of feature signals through the explicit representation of contrast-driven and luminance-driven information is provided and directly addresses the issue of brightness "anchoring." Computer simulations illustrate the model's competencies.Air Force Office of Scientific Research (F49620-92-J-0334); Northeast Consortium for Engineering Education (NCEE-A303-21-93); Office of Naval Research (N00014-91-J-4100); German BMFT grant (413-5839-01 1N 101 C/1); CNPq and NUTES/UFRJ, Brazi

Improving the Sharpness of Digital Images Using a Modified Laplacian Sharpening Technique

Many imaging systems produce images with deficient sharpness due to different real limitations. Hence, various image sharpening techniques have been used to improve the acutance of digital images. One of such is the well-known Laplacian sharpening technique. When implementing the basic Laplacian technique for image sharpening, two main drawbacks were detected. First, the amount of introduced sharpness cannot be increased or decreased. Second, in many situations, the resulted image suffers from a noticeable increase in brightness around the sharpened edges. In this article, an improved version of the basic Laplacian technique is proposed, wherein it contains two key modifications of weighting the Laplace operator to control the introduced sharpness and tweaking the second order derivatives to provide adequate brightness for recovered edges. To perform reliable experiments, only real-degraded images were used, and their accuracies were measured using a specialized no-reference image quality assessment metric. From the obtained experimental results, it is evident that the proposed technique outperformed the comparable techniques in terms of recorded accuracy and visual appearance

Image Enhancement using Guided Filter for under Exposed Images

Image enhancement becomes an important step to improve the quality of image and change in the appearance of the image in such a way that either a human or a machine can fetch certain information from the image after a change. Due to low contrast images it becomes very difficult to get any information out of it. In today’s digital world of imaging image enhancement is a very useful in various applications ranging from electronics printing to recognition. For highly underexposed region, intensity bin are present in darken region that’s by such images lacks in saturation and suffers from low intensity. Power law transformation provides solution to this problem. It enhances the brightness so as image at least becomes visible. To modify the intensity level histogram equalization can be used. In this we can apply cumulative density function and probabilistic density function so as to divide the image into sub images. In proposed approach to provide betterment in results guided filter has been applied to images after equalization so that we can get better Entropy rate and Coefficient of correlation can be improved with previously available techniques. The guided filter is derived from local linear model. The guided filter computes the filtering output by considering the content of guidance image, which can be the image itself or other targeted image

Extraction of low cost houses from a high spatial resolution satellite imagery using Canny edge detection filter

Since its democratic dispensation in 1994, the South African government enacted a number of legislative and policy interventions aimed at availing equal housing opportunities to the previously marginalized citizens. Mismanagement and unreliable reporting has been widely reported in publicly funded housing programmes which necessitated the government to audit and monitor housing development projects in municipalities using more robust and independent methodologies. The objective of this study was therefore to test and demonstrate the effectiveness of high spatial resolution satellite imagery in validating the presence of government funded houses using an object-oriented classification technique that applies a Canny edge detection filter. The results of this study demonstrate that object-orientated classification applied on pan-sharpened SPOT 6 satellite imagery can be used to conduct a reliable inventory and validate the number of houses. The application of the multi-resolution segmentation and Canny edge detection filtering technique proved to be an effective means of mapping individual houses as shown by the high detection accuracy of 99% and quality percentage of 96%.Keywords: Houses, Remote Sensing, SPOT 6, Canny edge detection, Multi-resolution Segmentation, Object-Oriented Classificatio

Automatic solar feature detection using image processing and pattern recognition techniques

The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Hα full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves the ability to monitor the evolution of solar events and has potential to be used to predict the space weather