282 research outputs found
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
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
Low-Light Hyperspectral Image Enhancement
Due to inadequate energy captured by the hyperspectral camera sensor in poor
illumination conditions, low-light hyperspectral images (HSIs) usually suffer
from low visibility, spectral distortion, and various noises. A range of HSI
restoration methods have been developed, yet their effectiveness in enhancing
low-light HSIs is constrained. This work focuses on the low-light HSI
enhancement task, which aims to reveal the spatial-spectral information hidden
in darkened areas. To facilitate the development of low-light HSI processing,
we collect a low-light HSI (LHSI) dataset of both indoor and outdoor scenes.
Based on Laplacian pyramid decomposition and reconstruction, we developed an
end-to-end data-driven low-light HSI enhancement (HSIE) approach trained on the
LHSI dataset. With the observation that illumination is related to the
low-frequency component of HSI, while textural details are closely correlated
to the high-frequency component, the proposed HSIE is designed to have two
branches. The illumination enhancement branch is adopted to enlighten the
low-frequency component with reduced resolution. The high-frequency refinement
branch is utilized for refining the high-frequency component via a predicted
mask. In addition, to improve information flow and boost performance, we
introduce an effective channel attention block (CAB) with residual dense
connection, which served as the basic block of the illumination enhancement
branch. The effectiveness and efficiency of HSIE both in quantitative
assessment measures and visual effects are demonstrated by experimental results
on the LHSI dataset. According to the classification performance on the remote
sensing Indian Pines dataset, downstream tasks benefit from the enhanced HSI.
Datasets and codes are available:
\href{https://github.com/guanguanboy/HSIE}{https://github.com/guanguanboy/HSIE}
Image Restoration
This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with
Unifying the Visible and Passive Infrared Bands: Homogeneous and Heterogeneous Multi-Spectral Face Recognition
Face biometrics leverages tools and technology in order to automate the identification of individuals. In most cases, biometric face recognition (FR) can be used for forensic purposes, but there remains the issue related to the integration of technology into the legal system of the court. The biggest challenge with the acceptance of the face as a modality used in court is the reliability of such systems under varying pose, illumination and expression, which has been an active and widely explored area of research over the last few decades (e.g. same-spectrum or homogeneous matching). The heterogeneous FR problem, which deals with matching face images from different sensors, should be examined for the benefit of military and law enforcement applications as well. In this work we are concerned primarily with visible band images (380-750 nm) and the infrared (IR) spectrum, which has become an area of growing interest.;For homogeneous FR systems, we formulate and develop an efficient, semi-automated, direct matching-based FR framework, that is designed to operate efficiently when face data is captured using either visible or passive IR sensors. Thus, it can be applied in both daytime and nighttime environments. First, input face images are geometrically normalized using our pre-processing pipeline prior to feature-extraction. Then, face-based features including wrinkles, veins, as well as edges of facial characteristics, are detected and extracted for each operational band (visible, MWIR, and LWIR). Finally, global and local face-based matching is applied, before fusion is performed at the score level. Although this proposed matcher performs well when same-spectrum FR is performed, regardless of spectrum, a challenge exists when cross-spectral FR matching is performed. The second framework is for the heterogeneous FR problem, and deals with the issue of bridging the gap across the visible and passive infrared (MWIR and LWIR) spectrums. Specifically, we investigate the benefits and limitations of using synthesized visible face images from thermal and vice versa, in cross-spectral face recognition systems when utilizing canonical correlation analysis (CCA) and locally linear embedding (LLE), a manifold learning technique for dimensionality reduction. Finally, by conducting an extensive experimental study we establish that the combination of the proposed synthesis and demographic filtering scheme increases system performance in terms of rank-1 identification rate
Unsupervised Low Light Image Enhancement Using SNR-Aware Swin Transformer
Image captured under low-light conditions presents unpleasing artifacts,
which debilitate the performance of feature extraction for many upstream visual
tasks. Low-light image enhancement aims at improving brightness and contrast,
and further reducing noise that corrupts the visual quality. Recently, many
image restoration methods based on Swin Transformer have been proposed and
achieve impressive performance. However, On one hand, trivially employing Swin
Transformer for low-light image enhancement would expose some artifacts,
including over-exposure, brightness imbalance and noise corruption, etc. On the
other hand, it is impractical to capture image pairs of low-light images and
corresponding ground-truth, i.e. well-exposed image in same visual scene. In
this paper, we propose a dual-branch network based on Swin Transformer, guided
by a signal-to-noise ratio prior map which provides the spatial-varying
information for low-light image enhancement. Moreover, we leverage unsupervised
learning to construct the optimization objective based on Retinex model, to
guide the training of proposed network. Experimental results demonstrate that
the proposed model is competitive with the baseline models
An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure
We present an exploration of the rich theoretical connections between several
classes of regularized models, network flows, and recent results in submodular
function theory. This work unifies key aspects of these problems under a common
theory, leading to novel methods for working with several important models of
interest in statistics, machine learning and computer vision.
In Part 1, we review the concepts of network flows and submodular function
optimization theory foundational to our results. We then examine the
connections between network flows and the minimum-norm algorithm from
submodular optimization, extending and improving several current results. This
leads to a concise representation of the structure of a large class of pairwise
regularized models important in machine learning, statistics and computer
vision.
In Part 2, we describe the full regularization path of a class of penalized
regression problems with dependent variables that includes the graph-guided
LASSO and total variation constrained models. This description also motivates a
practical algorithm. This allows us to efficiently find the regularization path
of the discretized version of TV penalized models. Ultimately, our new
algorithms scale up to high-dimensional problems with millions of variables
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