Sparse representation via optimal matching convolution framelets
نویسندگان
چکیده
منابع مشابه
Image Classification via Sparse Representation and Subspace Alignment
Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...
متن کاملDeblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation
JPEG is one of the most widely used image compression method, but it causes annoying blocking artifacts at low bit-rates. Sparse representation is an efficient technique which can solve many inverse problems in image processing applications such as denoising and deblocking. In this paper, a post-processing method is proposed for reducing JPEG blocking effects via sparse representation. In this ...
متن کاملOptimal representation of sparse matrices
This paper introduces a novel data structure used to store sparse matrices optimally – minimizing the space of the matrix representation and the time complexity of an access to the matrix element. The size of our data structure is close to the information theoretic minimum – it differs in the second order term – and permits constant access to the matrix elements and a constant amortized time wi...
متن کاملFraming U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
X-ray computed tomography (CT) using sparse projection views is often used to reduce the radiation dose. However, due to the insufficient projection views, a reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-net have demonstrated impressive performance for...
متن کاملDUDE for Natural Images via Sparse Representation
Discrete Universal Denoiser, or DUDE, is a method of removing noise from an arbitrary source sequence that has been corrupted by a known memoryless channel, without consideration of the source distribution. The canonical formulation of DUDE assumes a small discrete alphabet, from which source symbols are chosen. Extension of DUDE into a large-alphabet domain, or the continuous domain, has been ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IET Signal Processing
سال: 2018
ISSN: 1751-9675,1751-9683
DOI: 10.1049/iet-spr.2018.5108