Muhammad Rushdi

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Recent approaches in image super-resolution suggest learning dictionary pairs to model the relationship between low-resolution and high-resolution image patches with sparsity constraints on the patch representation. Most of the previous approaches in this direction assume for simplicity that the sparse codes for a low-resolution patch are equal to those of(More)
This paper proposes a novel approach for sparse coding that further improves upon the sparse representation-based classification (SRC) framework. The proposed framework, Affine-Constrained Group Sparse Coding (ACGSC), extends the current SRC framework to classification problems with multiple input samples. Geometrically, the affineconstrained group sparse(More)
This paper studies the following problem: Given an SVM (kernel)-based binary classifier C as a black-box oracle, how much can we learn of its internal working by querying it? Specifically, we assume the feature space R d is known and the kernel machine has m support vectors such that d > m (or d >> m), and in addition, the classifier C is laconic in the(More)
Consumer-level digital cameras typically post-process raw captured image data to produce enhanced visually appealing output RGB images. Post-processing operations include color gamut compression, tone mapping and other non-linear color corrections. However, raw image data is needed for many computer vision applications such as photometric stereo, shape from(More)
This paper proposes a novel algorithm for speckle reduction in medical ultrasound imaging while preserving the edges with the added advantages of adaptive noise filtering and speed. We propose a nonlinear image diffusion algorithm that incorporates two local parameters of image quality, namely, scatterer density and texture-based contrast in addition to(More)
2013 c ⃝ 2013 Shaoyu Qi 2 I dedicate this thesis to my beloved parents and other family members. Thank you for all of your constant support, encouragement and love throughout my life. 3 ACKNOWLEDGMENTS I would like to express my sincere thanks to everyone surrounds me. Their help, support and encouragement are indispensable for me to finish my Ph.D(More)