SONAR Images Denoising

International audienc

Image interpolation and denoising in discrete wavelet transform domain

Traditionally, processing a compressed image requires decompression first. Following the related manipulations, the processed image is compressed again for storage. To reduce the computational complexity and processing time, manipulating images in the transform domain, which is possible, is an efficient solution; The uniform wavelet thresholding is one of the most widely used methods for image denoising in the Discrete Wavelet Transform (DWT) domain. This method, however, has the drawback of blurring the edges and the textures of an image after denoising. A new algorithm is proposed in this thesis for image denoising in the DWT domain with no blurring effect. This algorithm uses a suite of feature extraction and image segmentation techniques to construct filter masks for denoising. The novelty of the algorithm is that it directly extracts the edges and texture details of an image from the spatial information contained in the LL subband of DWT domain rather than detecting the edges across multiple scales. An added advantage of this method is the substantial reduction in computational complexity. Experimental results indicate that the new algorithm would yield higher quality images (both qualitatively and quantitatively) than the existing methods; In this thesis, new algorithm for image interpolation in the DWT domain is also discussed. Being different from other methods for interpolation, which focus on Haar wavelet, new interpolation algorithm also investigates other wavelets, such as Daubecuies and Bior. Experimental results indicate that the new algorithm is superior to the traditional methods by comparing the time complexity and quality of the processed image

Multi-Modal Enhancement Techniques for Visibility Improvement of Digital Images

Image enhancement techniques for visibility improvement of 8-bit color digital images based on spatial domain, wavelet transform domain, and multiple image fusion approaches are investigated in this dissertation research. In the category of spatial domain approach, two enhancement algorithms are developed to deal with problems associated with images captured from scenes with high dynamic ranges. The first technique is based on an illuminance-reflectance (I-R) model of the scene irradiance. The dynamic range compression of the input image is achieved by a nonlinear transformation of the estimated illuminance based on a windowed inverse sigmoid transfer function. A single-scale neighborhood dependent contrast enhancement process is proposed to enhance the high frequency components of the illuminance, which compensates for the contrast degradation of the mid-tone frequency components caused by dynamic range compression. The intensity image obtained by integrating the enhanced illuminance and the extracted reflectance is then converted to a RGB color image through linear color restoration utilizing the color components of the original image. The second technique, named AINDANE, is a two step approach comprised of adaptive luminance enhancement and adaptive contrast enhancement. An image dependent nonlinear transfer function is designed for dynamic range compression and a multiscale image dependent neighborhood approach is developed for contrast enhancement. Real time processing of video streams is realized with the I-R model based technique due to its high speed processing capability while AINDANE produces higher quality enhanced images due to its multi-scale contrast enhancement property. Both the algorithms exhibit balanced luminance, contrast enhancement, higher robustness, and better color consistency when compared with conventional techniques. In the transform domain approach, wavelet transform based image denoising and contrast enhancement algorithms are developed. The denoising is treated as a maximum a posteriori (MAP) estimator problem; a Bivariate probability density function model is introduced to explore the interlevel dependency among the wavelet coefficients. In addition, an approximate solution to the MAP estimation problem is proposed to avoid the use of complex iterative computations to find a numerical solution. This relatively low complexity image denoising algorithm implemented with dual-tree complex wavelet transform (DT-CWT) produces high quality denoised images

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature

Unsupervised multi-scale change detection from SAR imagery for monitoring natural and anthropogenic disasters

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Radar remote sensing can play a critical role in operational monitoring of natural and anthropogenic disasters. Despite its all-weather capabilities, and its high performance in mapping, and monitoring of change, the application of radar remote sensing in operational monitoring activities has been limited. This has largely been due to: (1) the historically high costs associated with obtaining radar data; (2) slow data processing, and delivery procedures; and (3) the limited temporal sampling that was provided by spaceborne radar-based satellites. Recent advances in the capabilities of spaceborne Synthetic Aperture Radar (SAR) sensors have developed an environment that now allows for SAR to make significant contributions to disaster monitoring. New SAR processing strategies that can take full advantage of these new sensor capabilities are currently being developed. Hence, with this PhD dissertation, I aim to: (i) investigate unsupervised change detection techniques that can reliably extract signatures from time series of SAR images, and provide the necessary flexibility for application to a variety of natural, and anthropogenic hazard situations; (ii) investigate effective methods to reduce the effects of speckle and other noise on change detection performance; (iii) automate change detection algorithms using probabilistic Bayesian inferencing; and (iv) ensure that the developed technology is applicable to current, and future SAR sensors to maximize temporal sampling of a hazardous event. This is achieved by developing new algorithms that rely on image amplitude information only, the sole image parameter that is available for every single SAR acquisition. The motivation and implementation of the change detection concept are described in detail in Chapter 3. In the same chapter, I demonstrated the technique's performance using synthetic data as well as a real-data application to map wildfire progression. I applied Radiometric Terrain Correction (RTC) to the data to increase the sampling frequency, while the developed multiscaledriven approach reliably identified changes embedded in largely stationary background scenes. With this technique, I was able to identify the extent of burn scars with high accuracy. I further applied the application of the change detection technology to oil spill mapping. The analysis highlights that the approach described in Chapter 3 can be applied to this drastically different change detection problem with only little modification. While the core of the change detection technique remained unchanged, I made modifications to the pre-processing step to enable change detection from scenes of continuously varying background. I introduced the Lipschitz regularity (LR) transformation as a technique to normalize the typically dynamic ocean surface, facilitating high performance oil spill detection independent of environmental conditions during image acquisition. For instance, I showed that LR processing reduces the sensitivity of change detection performance to variations in surface winds, which is a known limitation in oil spill detection from SAR. Finally, I applied the change detection technique to aufeis flood mapping along the Sagavanirktok River. Due to the complex nature of aufeis flooded areas, I substituted the resolution-preserving speckle filter used in Chapter 3 with curvelet filters. In addition to validating the performance of the change detection results, I also provide evidence of the wealth of information that can be extracted about aufeis flooding events once a time series of change detection information was extracted from SAR imagery. A summary of the developed change detection techniques is conducted and suggested future work is presented in Chapter 6

Investigating Potential Combinations of Visual Features towards Improvement of Full-Reference and No-Reference Image Quality Assessment

Objective assessment of image quality is the process of automatic assignment of a scalar score to an image such that the rating or score corresponds to the score provided by the Human Visual System (HVS). Despite extensive studies since the last two decades, it remains a challenging problem in image processing due to the presence of different types of distortions and limited knowledge of the HVS. Existing approaches for assessing the perceptual quality of images have relied on a number of methodologies that directly apply known properties of the HVS, construct hypotheses considering the HVS as a blackbox and use hybrid approaches that apply both of the techniques. All of these methodologies have relied on different types of visual features for Image Quality Assessment (IQA). In this dissertation, we have studied the problem of different types of IQA from the feature extraction point of view and showed that effective combinations of simple visual features can be used to develop IQA approaches having competitive performance with the state-of-the-art. Our work is divided into four parts each having the final goal to bring about performance improvement in the areas of Full-Reference (FR) and No-Reference (NR)-IQA. We have gradually moved from FR to NR-IQA in the works presented in this dissertation. First, we propose improvements in two existing FR-IQA techniques by introducing changes in the features used. Next, we propose a new FR-IQA technique by extracting image saliency as global features and combining them with the local features of gradient and variance to improve the performance. For NR-IQA, we propose a novel technique for sharpness detection in natural images using simple features. The performance of this method provides improvement over the existing methods. After working with the specific purpose NR-IQA, we propose a general purpose technique using suitable features such that no training with pristine or distorted images or subjective quality scores is required. This technique, despite having no reliance on training, provides competitive performance with the state-of-the-art techniques. The main contribution of the dissertation lies in identification and analysis of effective features and their combinations for improving three different sub-areas of IQA

Multisensor Concealed Weapon Detection Using the Image Fusion Approach

Detection of concealed weapons is an increasingly important problem for both military and police since global terrorism and crime have grown as threats over the years. This work presents two image fusion algorithms, one at pixel level and another at feature level, for efficient concealed weapon detection application. Both the algorithms presented in this work are based on the double-density dual-tree complex wavelet transform (DDDTCWT). In the pixel level fusion scheme, the fusion of low frequency band coefficients is determined by the local contrast, while the high frequency band fusion rule is developed with consideration of both texture feature of the human visual system (HVS) and local energy basis. In the feature level fusion algorithm, features are exacted using Gaussian Mixture model (GMM) based multiscale segmentation approach and the fusion rules are developed based on region activity measurement. Experiment results demonstrate the robustness and efficiency of the proposed algorithms

Signal processing algorithms for enhanced image fusion performance and assessment

The dissertation presents several signal processing algorithms for image fusion in noisy multimodal conditions. It introduces a novel image fusion method which performs well for image sets heavily corrupted by noise. As opposed to current image fusion schemes, the method has no requirements for a priori knowledge of the noise component. The image is decomposed with Chebyshev polynomials (CP) being used as basis functions to perform fusion at feature level. The properties of CP, namely fast convergence and smooth approximation, renders it ideal for heuristic and indiscriminate denoising fusion tasks. Quantitative evaluation using objective fusion assessment methods show favourable performance of the proposed scheme compared to previous efforts on image fusion, notably in heavily corrupted images. The approach is further improved by incorporating the advantages of CP with a state-of-the-art fusion technique named independent component analysis (ICA), for joint-fusion processing based on region saliency. Whilst CP fusion is robust under severe noise conditions, it is prone to eliminating high frequency information of the images involved, thereby limiting image sharpness. Fusion using ICA, on the other hand, performs well in transferring edges and other salient features of the input images into the composite output. The combination of both methods, coupled with several mathematical morphological operations in an algorithm fusion framework, is considered a viable solution. Again, according to the quantitative metrics the results of our proposed approach are very encouraging as far as joint fusion and denoising are concerned. Another focus of this dissertation is on a novel metric for image fusion evaluation that is based on texture. The conservation of background textural details is considered important in many fusion applications as they help define the image depth and structure, which may prove crucial in many surveillance and remote sensing applications. Our work aims to evaluate the performance of image fusion algorithms based on their ability to retain textural details from the fusion process. This is done by utilising the gray-level co-occurrence matrix (GLCM) model to extract second-order statistical features for the derivation of an image textural measure, which is then used to replace the edge-based calculations in an objective-based fusion metric. Performance evaluation on established fusion methods verifies that the proposed metric is viable, especially for multimodal scenarios

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations