4.4 Application: Structure Denoising In sparse representations, the simplest denoising methods are performed by a simple thresholding of the discrete curvelet coefficients. The threshold level is usually taken a
It is well established that high-level representations learned via sparse coding are effective for many machine learning applications such as denoising and classification. In addition to being reconstructive, sparse representations that are discriminative and invariant can further help with such applications...
When the error between the vector and its sparse approximation meets the conditions, OMP calculation will be stopped. Among them, the gain factor C is used to balance the fidelity and sparsity, and the gain factor makes the sparse coding step more flexible. The denoising gain factor balances ...
This local kernel regression method, which can be implemented iteratively to improve the quality of the entire image, has been proven effective in the application of image inpainting, denoising, fusion, and interpolation. In addition, inspired by the fact that natural images often contain repetitive...
BM3D is a state-of-the-art image denoising method. Its denoised results in the regions with strong edges can often be better than in the regions with smooth or weak edges, due to more accurate block-matching for the strong-edge regions. So using adaptive
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24 DnCNN 928 450 MATLAB 31 Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017) 2020-09-05T12:05:22Z 25 convnet-burden 795 107 MATLAB 4 Memory consumption and FLOP count estimates for convnets 2019-01-17T11:15:00Z 26 Coursera-Machi...
Such transforms are useful for coding, denoising, and image restoration using sparse signal representation techniques. This paper describes a new non-separable 2D DCT-like orthonormal block transform that is optimized for a specified orientation angle. The approach taken in this paper is to extend ...