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Updates found with 'deconvolution'

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Updates found with 'deconvolution'

MATLAB PROJECTS ABSTRACT 2016-2017 LEARNING ITERATION-WISE GENERALIZED SHRINKAGE-THRESHOLDING OPERATORS FOR BLIND DECONVOLUTION ABSTRACT: Salient edge selection and time-varying regularization are two crucial techniques to guarantee the success of maximum a posterior (MAP)-based blind convolution. However, the existing approaches usually rely on carefully designed regularizes and handcrafted parameter tuning to obtain satisfactory estimation of the blur kernel. Many regularizes exhibit the structure-preserving smoothing capability, but fail to enhance salient edges. In this paper, under the MAP framework, we propose the iteration-wise `p-norm regularizes together with data-driven strategy to address these issues. First, we extend the generalized shrinkage-thresholding (GST) operator for `pnorm minimization with negative p value, which can sharpen salient edges while suppressing trivial details. Then, the iteration wise GST parameters are specified to allow dynamical salient edge selection and time-varying regularization. Finally, instead of handcrafted tuning, a principled discriminative learning approach is proposed to learn the iteration-wise GST operators from the training dataset. Furthermore, the multi-scale scheme is developed to improve the efficiency of the algorithm. Experimental results show that, negative p value is more effective in estimating the coarse shape of blur kernel at the early stage, and the learned GST operators can be well generalized to other data set and real world blurry images. Compared with the state of-the-art methods, our method achieves better deblurring results in terms of both quantitative metrics and visual quality, and it is much faster than the state-of-the-art patch-based blind deconvolution method.
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IEEE 2016 - 2017 Matlab Image Processing TitlesS.No Project Titles 1. Data-driven Soft Decoding of Compressed Images in Dual Transform-Pixel Domain 2. Double-Tip Arte fact Removal from Atomic Force Microscopy Images 3. Quaternion Collaborative and Sparse Representation With Application to Color Face Recognition 4. Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation 5. Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation 6. Robust Visual Tracking via Convolutional Networks without Training 7. Context-based prediction filtering of impulse noise images 8. Predicting the Forest Fire Using Image Processing 9. A Review Paper on detection of Glaucoma using Retinal Fundus Images 10. Performance Analysis of Filters on Complex Images for Text Extraction through Binarization 11. Automated Malaria Detection from Blood Samples Using Image Processing 12. Learning Invariant Color Features for Person Re-Identification 13. A Diffusion and Clustering-based Approach for Finding Coherent Motions and Understanding Crowd Scenes 14. Automatic Design of Color Filter Arrays in The Frequency Domain 15. Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution 16. Image Segmentation Using Parametric Contours With Free Endpoints 17. CASAIR: Content and Shape-Aware Image Retargeting and Its Applications 18. Texture classification using Dense Micro-block Difference 19. Statistical performance analysis of a fast super-resolution technique using noisy translations 20. Trees Leaves Extraction In Natural Images Based On Image segmentation and generating Its plant details
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IEEE 2017-2018 SIGNAL PROCESSING TITLES ESTIMATION OF RESPIRATORY PATTERN FROM VIDEO USING SELECTIVE ENSEMBLE AGGREGATIONAbstract:- Non-contact estimation of respiratory pattern (RP) and respiratory rate (RR) has multiple applications. Existing methods for RP and RR measurement fall into one of the three categories - (i) estimation through nasal air flow measurement, (ii) estimation from video-based remote photoplethysmography, and (iii) estimation by measurement of motion induced by respiration using motion detectors. However, these methods require specialized sensors, are computationally expensive and/or critically depend on selection of a region of interest (ROI) for processing. In this paper, a general framework is described for estimating a periodic signal driving noisy linear time-invariant (LTI) channels connected in parallel with unknown dynamics. The method is then applied to derive a computationally inexpensive method for estimating RP using 2D cameras that does not critically depend on ROI. Specifically, RP is estimated by imaging changes in the reflected light caused by respiration-induced motion. Each spatial location in the field of view of the camera is modeled as a noise-corrupted LTI measurement channel with unknown system dynamics, driven by a single generating respiratory signal. Estimation of RP is cast as a blind deconvolution problem and is solved through a method comprising subspace projection and statistical aggregation. Experiments are carried out on 31 healthy human subjects by generating multiple RPs and comparing the proposed estimates with simultaneously acquired ground truth from an impedance pneumograph device. The proposed estimator agrees well with the ground truth in terms of correlation measures, despite variability in clothing pattern, camera angle and ROI.
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