3,749 research outputs found

    Video enhancement : content classification and model selection

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    The purpose of video enhancement is to improve the subjective picture quality. The field of video enhancement includes a broad category of research topics, such as removing noise in the video, highlighting some specified features and improving the appearance or visibility of the video content. The common difficulty in this field is how to make images or videos more beautiful, or subjectively better. Traditional approaches involve lots of iterations between subjective assessment experiments and redesigns of algorithm improvements, which are very time consuming. Researchers have attempted to design a video quality metric to replace the subjective assessment, but so far it is not successful. As a way to avoid heuristics in the enhancement algorithm design, least mean square methods have received considerable attention. They can optimize filter coefficients automatically by minimizing the difference between processed videos and desired versions through a training. However, these methods are only optimal on average but not locally. To solve the problem, one can apply the least mean square optimization for individual categories that are classified by local image content. The most interesting example is Kondo’s concept of local content adaptivity for image interpolation, which we found could be generalized into an ideal framework for content adaptive video processing. We identify two parts in the concept, content classification and adaptive processing. By exploring new classifiers for the content classification and new models for the adaptive processing, we have generalized a framework for more enhancement applications. For the part of content classification, new classifiers have been proposed to classify different image degradations such as coding artifacts and focal blur. For the coding artifact, a novel classifier has been proposed based on the combination of local structure and contrast, which does not require coding block grid detection. For the focal blur, we have proposed a novel local blur estimation method based on edges, which does not require edge orientation detection and shows more robust blur estimation. With these classifiers, the proposed framework has been extended to coding artifact robust enhancement and blur dependant enhancement. With the content adaptivity to more image features, the number of content classes can increase significantly. We show that it is possible to reduce the number of classes without sacrificing much performance. For the part of model selection, we have introduced several nonlinear filters to the proposed framework. We have also proposed a new type of nonlinear filter, trained bilateral filter, which combines both advantages of the original bilateral filter and the least mean square optimization. With these nonlinear filters, the proposed framework show better performance than with linear filters. Furthermore, we have shown a proof-of-concept for a trained approach to obtain contrast enhancement by a supervised learning. The transfer curves are optimized based on the classification of global or local image content. It showed that it is possible to obtain the desired effect by learning from other computationally expensive enhancement algorithms or expert-tuned examples through the trained approach. Looking back, the thesis reveals a single versatile framework for video enhancement applications. It widens the application scope by including new content classifiers and new processing models and offers scalabilities with solutions to reduce the number of classes, which can greatly accelerate the algorithm design

    BLADE: Filter Learning for General Purpose Computational Photography

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    The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization

    Dual modality optical coherence tomography : Technology development and biomedical applications

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    Optical coherence tomography (OCT) is a cross-sectional imaging modality that is widely used in clinical ophthalmology and interventional cardiology. It is highly promising for in situ characterization of tumor tissues. OCT has high spatial resolution and high imaging speed to assist clinical decision making in real-time. OCT can be used in both structural imaging and mechanical characterization. Malignant tumor tissue alters morphology. Additionally, structural OCT imaging has limited tissue differentiation capability because of the complex and noisy nature of the OCT signal. Moreover, the contrast of structural OCT signal derived from tissue’s light scattering properties has little chemical specificity. Hence, interrogating additional tissue properties using OCT would improve the outcome of OCT’s clinical applications. In addition to morphological difference, pathological tissue such as cancer breast tissue usually possesses higher stiffness compared to the normal healthy tissue, which indicates a compelling reason for the specific combination of structural OCT imaging with stiffness assessment in the development of dual-modality OCT system for the characterization of the breast cancer diagnosis. This dissertation seeks to integrate the structural OCT imaging and the optical coherence elastography (OCE) for breast cancer tissue characterization. OCE is a functional extension of OCT. OCE measures the mechanical response (deformation, resonant frequency, elastic wave propagation) of biological tissues under external or internal mechanical stimulation and extracts the mechanical properties of tissue related to its pathological and physiological processes. Conventional OCE techniques (i.e., compression, surface acoustic wave, magnetomotive OCE) measure the strain field and the results of OCE measurement are different under different loading conditions. Inconsistency is observed between OCE characterization results from different measurement sessions. Therefore, a robust mechanical characterization is required for force/stress quantification. A quantitative optical coherence elastography (qOCE) that tracks both force and displacement is proposed and developed at NJIT. qOCE instrument is based on a fiber optic probe integrated with a Fabry-Perot force sensor and the miniature probe can be delivered to arbitrary locations within animal or human body. In this dissertation, the principle of qOCE technology is described. Experimental results are acquired to demonstrate the capability of qOCE in characterizing the elasticity of biological tissue. Moreover, a handheld optical instrument is developed to allow in vivo real-time OCE characterization based on an adaptive Doppler analysis algorithm to accurately track the motion of sample under compression. For the development of the dual modality OCT system, the structural OCT images exhibit additive and multiplicative noises that degrade the image quality. To suppress noise in OCT imaging, a noise adaptive wavelet thresholding (NAWT) algorithm is developed to remove the speckle noise in OCT images. NAWT algorithm characterizes the speckle noise in the wavelet domain adaptively and removes the speckle noise while preserving the sample structure. Furthermore, a novel denoising algorithm is also developed that adaptively eliminates the additive noise from the complex OCT using Doppler variation analysis

    Bilateral filter in image processing

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    The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. It also can be applied to the blocking artifacts reduction. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. Another research interest of bilateral filter is acceleration of the computation speed. There are three main contributions of this thesis. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising. I propose an extension of the bilateral filter: multi resolution bilateral filter, where bilateral filtering is applied to the low-frequency sub-bands of a signal decomposed using a wavelet filter bank. The multi resolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. The second contribution is that I present a spatially adaptive method to reduce compression artifacts. To avoid over-smoothing texture regions and to effectively eliminate blocking and ringing artifacts, in this paper, texture regions and block boundary discontinuities are first detected; these are then used to control/adapt the spatial and intensity parameters of the bilateral filter. The test results prove that the adaptive method can improve the quality of restored images significantly better than the standard bilateral filter. The third contribution is the improvement of the fast bilateral filter, in which I use a combination of multi windows to approximate the Gaussian filter more precisely
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