Estudo para otimização do algoritmo Non-local means visando aplicações em tempo real

• Ano de defesa: 2014
• Autor(a) principal: Silva, Hamilton Soares da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraí­ba; BR; Engenharia Mecânica; Programa de Pós Graduação em Engenharia Mecânica; UFPB
• Palavras-chave em Português: Processamento Digital de Imagens; Redução de Ruído; Computação Reconfigurável; Computação Paralela; Programação GPU CUDA; Digital Image Processing; Image Denoising; Reconfigurable Computing; Parallel Computing; Programming CUDA GPU; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/5383

The aim of this work is to study the non-local means algorithm and propose techniques to optimize and implement this algorithm for its application in real-time. Two alternatives are suggested for implementation. The first deals with the development of an accelerator card for computers, which has a PCI bus containing specialized hardware that implements the NLM filter. The second implementation uses densely GPU multiprocessor environment, which exists in the parent video. Both proposals significantly accelerates the NLM algorithm, while maintains the same visual quality of traditional software implementations, enabling real-time use. Image denoising is an important area for digital image processing. Recently, its use is becoming more popular due to improvements of of the new acquisition equipments and, thus, the increase of image resolution that favors the occurrence of such perturbations. It is widely studied in the fields of image processing, computer vision and predictive maintenance of electrical substations, motors, tires, building facilities, pipes and fittings, focusing on reducing the noise without removing details of the original image. Several approaches have been proposed for filtering noise. One of such approaches is the non-local method called Non-Local Means (NLM), which uses the entire image rather than local information and stands out as the state of the art. However, a problem in this method is its high computational complexity, which turns its application almost impossible in real time applications, even for small images