Comparison of compressed sensing based algorithms for sparse signal reconstruction

Comparison of iterative algorithms for parameter estimation in nonlinear regression Musluoğlu, Gamze; Akkaya, Ayşen; Department of Statistics (2018) Nonlinear regression models are more common as compared to linear ones for real life cases e.g. climatology, biology, earthquake engineering, economics etc. However, nonlinear regression models are much more complex to fit and to interpret. Classical parameter estimation methods such as least squares and maximum likelihood can also be adopted to fit the model in nonlinear regression as well, but explicit solutions can not be achieved unlike linear models. At this point, iterative algorithms are utilized to ...
Comparison of multidimensional data access methods for feature-based image retrieval Arslan, Serdar; Saçan, Ahmet; Açar, Esra; Toroslu, İsmail Hakkı; Yazıcı, Adnan (2010-11-18) Within the scope of information retrieval, efficient similarity search in large document or multimedia collections is a critical task. In this paper, we present a rigorous comparison of three different approaches to the image retrieval problem, including cluster-based indexing, distance-based indexing, and multidimensional scaling methods. The time and accuracy tradeoffs for each of these methods are demonstrated on a large Corel image database. Similarity of images is obtained via a featurebased similarity...
Comparison of Dictionary-Based Image Reconstruction Algorithms for Inverse Problems Dogan, Didem; Öktem, Sevinç Figen (2020-10-07) Many inverse problems in imaging involve measurements that are in the form of convolutions. Sparsity priors are widely exploited in their solutions for regularization as these problems are generally ill-posed. In this work, we develop image reconstruction methods for these inverse problems using patchbased and convolutional sparse models. The resulting regularized inverse problems are solved via the alternating direction method of multipliers (ADMM). The performance of the developed algorithms is investigat...
Comparison of computational image inpainting methods Kurt, Cande; Batmaz, İnci; Ulusoy, İlkay; Department of Statistics (2018) Image processing plays an important role in today’s world. It has been using in medicine, quality control, defense industry, fine arts to ease for our lives. There are many applications in these fields such as tumor detection, license plate detection, edge detection, recognition of handwritten digits, filtering for noise reduction, restoring old photographs, and the like. The aim of image processing can be divided into five groups: visualization to observe the objects that are not visible, image sharpening ...
Comparison of decoding algorithms for low-density parity-check codes Kolaylı, Mert; Yücel, Melek D; Department of Electrical and Electronics Engineering (2006) Low-density parity-check (LDPC) codes are a subclass of linear block codes. These codes have parity-check matrices in which the ratio of the non-zero elements to all elements is low. This property is exploited in defining low complexity decoding algorithms. Low-density parity-check codes have good distance properties and error correction capability near Shannon limits. In this thesis, the sum-product and the bit-flip decoding algorithms for low-density parity-check codes are implemented on Intel Pentium M 1...

Citation Formats

S. Çelik, S. ERKÜÇÜK, and H. A. ÇIRPAN, “Comparison of compressed sensing based algorithms for sparse signal reconstruction,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/69186.