Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 10 of 10 for “"Sparse image"”.
-
Dictionary learning for scalable sparse image representation
… one of the novel processing tools is signal sparse coding which represents signals as linear combinations of a few representational basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and …
-
Sparse and Redundant Image Representations Using Adaptive Dictionaries in Digital Image Denoising
Digital image denoising is a widely know problem in image processing. In this work, we focus on removing additive white Gaussian noise from a given image. We use a denoising method that was extended from the Sparseland model. This method builds sparse image patch representations using redundant …
-
Nonparametric Bayesian Dictionary Learning and Count and Mixture Modeling
… In particular, we study dictionary learning for sparse image representation using the beta process and the dependent hierarchical beta process, and we present the negative binomial process, a novel nonparametric Bayesian prior that unites the seemingly disjoint problems of count and mixture …
-
Compressive sensing based imaging via belief propagation
… Sensing (CS) mainly aims at restoring an image from a small subset of samples with reasonable accuracy using an iterative message passing decoding algorithm commonly known as Belief Propagation (BP). The CS technique can accurately recover any compressible or sparse signal from a lesser …
-
Improving utilization, granularity, and interpretability in visual representation learning
… matching problem, where the generated image must match the feature distribution of the style image within the same hidden layers of the pretrained model. To that end, we propose using statistical moments as metrics for assessing distribution matching. Current style transfer methods …
-
Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
-
Deconvolution and sparsity based image restoration
Deconvolution and sparse representation are the two key areas in image and signal processing. In this thesis the classical image restoration problem is addressed using these two modalities. Image restoration, such as deblurring, dnoising, and in-painting belongs to the class of ill-posed linear …
-
Bridging Mri Reconstruction Across Eras: From Novel Optimization Of Traditional Methods To Efficient Deep Learning Strategies
… the redundancy among these coils were used for image reconstruction. Following the clinical impact and success of PI methods, compressed sensing (CS) techniques were developed to reconstruct images by using compressibility of images in a pre-specified linear transform domain. Transform learning …
-
Hardware acceleration for sparse Fourier image reconstruction
… important medical imaging algorithm: iterative sparse Fourier image reconstruction. We transform the algorithm to exploit massive parallelism available in the FPGA fabric. Our design allows different ways of chaining custom pipelined vector engines, so that different computations can be carried …
-
Motion compensation from limited data for reference-constrained image reconstruction
When reconstructing images from limited (or sparsely sampled) data, reference (or template) images are useful for constraining image reconstruction for various applications. However, in order to be an effective constraint, the reference should be correctly aligned with the target image one wants to …