Context-based adaptive image resolution upconversion

Context-based adaptive image resolution upconversion

bstract. We propose a practical context-based adaptive image esolution upconversion algorithm. The basic idea is to use a lowesolution (LR) image patch as a context in which the missing highesolution (HR) pixels are estimated. The context is quantized into lasses and for each class an adaptive linear filter is designed using training set. The training set incorporates the prior knowledge of he ...

Adaptive Resolution Upconversion for Compressed Video Using Pixel Classification

A novel adaptive resolution upconversion algorithm that is robust to compression artifacts is proposed. This method is based on classification of local image patterns using both structure information and activity measure to explicitly distinguish pixels into content or coding artifacts. The structure information is represented by adaptive dynamic-range coding and the activity measure is the com...

High resolution 2-D image upconversion of incoherent light

context driven concept based image retrieval

several semantic image search schemes have been recently proposed to retrieve images from the web. however, the query context is regularly ignored in these techniques and hence, many of the returned images are not adequately relevant. in this paper, we make use of context to further confine the outcome of the semantic search engines. for this purpose, we propose a hybrid search engine which uti...

Image-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 ...