335,264 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
Nonlinear Image Enhancement and Super Resolution for Enhanced Object Tracking
Tracking objects, such as vehicles and humans, in wide area motion imagery (WAMI) is a challenging problem because of the limited pixel area and the low contrast/visibility of the target objects. We propose an approach to make automatic tracking algorithms more effective by incorporating image enhancement and super resolution as preprocessing algorithms. The enhancement process includes the stages of dynamic range compression and contrast enhancement. Dynamic range compression is performed by a neighborhood based nonlinear intensity transformation process, which utilizes a locally tuned inverse sine nonlinear function to generate various nonlinear curves based on pixel’s neighborhood information. These nonlinear curves are used to select the new intensity value for each pixel. A contrast enhancement technique is used to maintain or improve the contrast of the original image. Local contrast enhancement using surrounding pixel information aids in extracting higher number of features a detector can find in the image, and therefore, improves the automatic object detection capabilities. Secondly, the super resolution technique is performed on an area surrounding the object of interest to increase the size of the object in terms of pixels. The single image super resolution process is performed in the Fourier phase space which preserves the local structure of each pixel in order to estimate the interpolated pixels in the high resolution image. As a result, super resolution increases the sharpness of edges and allows for addition tracking features to be extracted. The combination of these two techniques provides the necessary preprocessing enhancement to increase the effectiveness of tracking algorithms. A quantitative evaluation is performed to compare the results of the tracking with and without the proposed techniques. The analysis is based on results of an automatic detection and tracking technique, Gaussian Ringlet Intensity Distribution (GRID), evaluated using wide area motion imagery data.https://ecommons.udayton.edu/stander_posters/1481/thumbnail.jp
Wavelet-Based Enhancement Technique for Visibility Improvement of Digital Images
Image enhancement techniques for visibility improvement of color digital images based on wavelet transform domain are investigated in this dissertation research.
In this research, a novel, fast and robust wavelet-based dynamic range compression and local contrast enhancement (WDRC) algorithm to improve the visibility of digital images captured under non-uniform lighting conditions has been developed. A wavelet transform is mainly used for dimensionality reduction such that a dynamic range compression with local contrast enhancement algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also modified to prevent edge deformation. The inverse wavelet transform is carried out resulting in a lower dynamic range and contrast enhanced intensity image. A color restoration process based on the relationship between spectral bands and the luminance of the original image is applied to convert the enhanced intensity image back to a color image. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some pathological scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for tackling the color constancy problem. The illuminant is modeled having an effect on the image histogram as a linear shift and adjust the image histogram to discount the illuminant. The WDRC algorithm is then applied with a slight modification, i.e. instead of using a linear color restoration, a non-linear color restoration process employing the spectral context relationships of the original image is applied. The proposed technique solves the color constancy issue and the overall enhancement algorithm provides attractive results improving visibility even for scenes with near-zero visibility conditions.
In this research, a new wavelet-based image interpolation technique that can be used for improving the visibility of tiny features in an image is presented. In wavelet domain interpolation techniques, the input image is usually treated as the low-pass filtered subbands of an unknown wavelet-transformed high-resolution (HR) image, and then the unknown high-resolution image is produced by estimating the wavelet coefficients of the high-pass filtered subbands. The same approach is used to obtain an initial estimate of the high-resolution image by zero filling the high-pass filtered subbands. Detail coefficients are estimated via feeding this initial estimate to an undecimated wavelet transform (UWT). Taking an inverse transform after replacing the approximation coefficients of the UWT with initially estimated HR image, results in the final interpolated image.
Experimental results of the proposed algorithms proved their superiority over the state-of-the-art enhancement and interpolation techniques
Super resolution and dynamic range enhancement of image sequences
Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video
Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has
been explored recently, to convert legacy low resolution (LR) standard dynamic
range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for
the growing need of UHD HDR TV/broadcasting applications. However, previous
CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames,
and are only trained with a simple L2 loss. In this paper, we take a
divide-and-conquer approach in designing a novel GAN-based joint SR-ITM
network, called JSI-GAN, which is composed of three task-specific subnets: an
image reconstruction subnet, a detail restoration (DR) subnet and a local
contrast enhancement (LCE) subnet. We delicately design these subnets so that
they are appropriately trained for the intended purpose, learning a pair of
pixel-wise 1D separable filters via the DR subnet for detail restoration and a
pixel-wise 2D local filter by the LCE subnet for contrast enhancement.
Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss
alongside the conventional GAN loss, which helps enhancing both local details
and contrasts to reconstruct high quality HR HDR results. When all subnets are
jointly trained well, the predicted HR HDR results of higher quality are
obtained with at least 0.41 dB gain in PSNR over those generated by the
previous methods.Comment: The first two authors contributed equally to this work. Accepted at
AAAI 2020. (Camera-ready version
Bit-depth enhancement detection for compressed video
In recent years, display intensity and contrast have increased considerably.
Many displays support high dynamic range (HDR) and 10-bit color depth. Since
high bit-depth is an emerging technology, video content is still largely shot
and transmitted with a bit depth of 8 bits or less per color component.
Insufficient bit-depths produce distortions called false contours or banding,
and they are visible on high contrast screens. To deal with such distortions,
researchers have proposed algorithms for bit-depth enhancement
(dequantization). Such techniques convert videos with low bit-depth (LBD) to
videos with high bit-depth (HBD). The quality of converted LBD video, however,
is usually lower than that of the original HBD video, and many consumers prefer
to keep the original HBD versions. In this paper, we propose an algorithm to
determine whether a video has undergone conversion before compression. This
problem is complex; it involves detecting outcomes of different dequantization
algorithms in the presence of compression that strongly affects the
least-significant bits (LSBs) in the video frames. Our algorithm can detect
bit-depth enhancement and demonstrates good generalization capability, as it is
able to determine whether a video has undergone processing by dequantization
algorithms absent from the training dataset
A Video Upgradation of Low Vision AVI Video by Individual Pixel Channel Intensity Measurement and Its Enhancement
From the past few decades, the researchers and scholars have done the quality work in video and image processing and a wide range of outcomes has been discover and invented including the resolutions and sensitivity. Apart from these work there are many aspects are still hidden such as record a high dynamic range images and videos in low-light conditions especially when light is very low. When the intensity of noise is greater than the signal then the traditional denoising techniques cannot done their work properly. For this problem, many approaches being designed and developed to enhance the low-light video but Low contrast and noise remains a barrier to visually pleasing videos in low light conditions. To capture the videos in social gatherings, concerts, parties, musical events, dark forest and in security monitoring situations are still unsolved problem. In such conditions the video enhancement of low light video is really a tedious and tough job. This paper is proposing a new approach of video enhancement. The work is further going on to find a technique for better visibility of video
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