Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal - 2018 PROJECT TITLE :Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse Noise Removal - 2018ABSTRACT:Recently, the annihilating filter-primarily based low-rank Hankel matrix (ALOHA) approach was proposed as a powerful image inpainting technique. Based mostly on the observation that smoothness or textures within a picture patch correspond to sparse spectral parts within the frequency domain, ALOHA exploits the existence of annihilating filters and therefore the associated rank-deficient Hankel matrices in a picture domain to estimate any missing pixels. By extending this idea, we propose a unique impulse-noise removal algorithm that uses the sparse and low-rank decomposition of a Hankel structured matrix. This technique, known as the sturdy ALOHA, relies on the observation that a picture corrupted with the impulse noise has intact pixels; consequently, the impulse noise will be modeled as sparse components, whereas the underlying image can still be modeled employing a low-rank Hankel structured matrix. To solve the sparse and low-rank matrix decomposition problem, we tend to propose an alternating direction methodology of multiplier approach, with initial factorized matrices coming from a coffee-rank matrix-fitting algorithm. To adapt native image statistics that have distinct spectral distributions, the sturdy ALOHA is applied during a patch-by-patch manner. Experimental results from impulse noise for each single-channel and multichannel color images demonstrate that the sturdy ALOHA is superior to existing approaches, particularly throughout the reconstruction of complex texture patterns. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Single Image Super-Resolution Based on Wiener Filter in Similarity Domain - 2018 Spatial and Angular Resolution Enhancement of Light Fields Using Convolutional Neural Networks - 2018