Sparsity-Based Color Image Super Resolution via Exploiting Cross Channel Constraints
نویسندگان
چکیده
منابع مشابه
Image Super-Resolution Reconstruction Based On L1/2 Sparsity
Based on image sparse representation in the shearlet domain, we proposed a 2 1 L sparsity regularized unconvex variation model for image super-resolution. The 2 1 L regularizer term constrains the underlying image to have a sparse representation in shearlet domain. The fidelity term restricts the consistency with the measured imaged in terms of the data degradation model. Then, the variable spl...
متن کاملImage-adaptive Color Super-resolution
Image super-resolution is the problem of recovering a high resolution (hi-res) image from multiple low resolution (lo-res) acquisitions of a scene. The main focus and the most significant contributions of research in this area have been on the problem of super-resolving single channel (grayscale) images. Multi-channel (color) image super-resolution is often treated as an extension to grayscale ...
متن کاملSingle-Image Super-Resolution Using Sparsity Constraints and Non-Local Similarities at Multiple Resolution Scales
Traditional super-resolution methods produce a clean high-resolution image from several observed degraded low-resolution images following an acquisition or degradation model. Such a model describes how each output pixel is related to one or more input pixels and it is called data fidelity term in the regularization framework. Additionally, prior knowledge such as piecewise smoothness can be inc...
متن کاملImage Super-Resolution Based on Sparsity Prior via Smoothed l0 Norm
In this paper we aim to tackle the problem of reconstructing a high-resolution image from a single low-resolution input image, known as single image super-resolution. In the literature, sparse representation has been used to address this problem, where it is assumed that both low-resolution and high-resolution images share the same sparse representation over a pair of coupled jointly trained di...
متن کاملSparsity-Based Super Resolution for SEM Images
The scanning electron microscope (SEM) is an electron microscope that produces an image of a sample by scanning it with a focused beam of electrons. The electrons interact with the atoms in the sample, which emit secondary electrons that contain information about the surface topography and composition. The sample is scanned by the electron beam point by point, until an image of the surface is f...
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: IEEE Transactions on Image Processing
سال: 2017
ISSN: 1057-7149,1941-0042
DOI: 10.1109/tip.2017.2704443