My Account | Careers | Downloads | Blog

PROJECT TITLE :

Color Demosaicking Via Nonlocal Tensor Representation - 2017

ABSTRACT:

One sensor camera can capture scenes by means that of color filter array. Each pixel samples solely one amongst the 3 primary colours. Color demosaicking (CDM) could be a method of reconstruction a full color image from this sensor data. In this paper, we propose a unique CDM scheme based mostly on learned simultaneous sparse coding over nonlocal tensor representation. First, similar 2D patches are grouped to form a three-order tensor, that is, 3D array. Then, three sub-dictionaries, which characterize the coherent structures that appear in each dimension of the grouped tensor, are learned jointly by using Tucker decomposition. The consequent coefficient tensor is imposed by the grouped-block-sparsity constraint, which forces the similar patches to share the same atoms of the dictionaries in their sparse decomposition. Experimental results demonstrate the effectiveness both in the average CPSNR and visual quality.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : A Framework of Reversible Color to Grayscale Conversion With Watermarking Feature ABSTRACT: In order to preserve the original colour image, reversible color-to-grayscale conversion (RCGC) is used to encode the
PROJECT TITLE : Compressive Color Pattern Detection Using Partial Orthogonal Circulant Sensing Matrix ABSTRACT: To get acceptable signal reconstruction quality with compressive sensing, it's important to create a sensing matrix
PROJECT TITLE : Hazy Image Decolorization With Color Contrast Restoration ABSTRACT: Because the colour contrast field in a foggy image is warped, converting it to grayscale is difficult. We propose a technique for decolorizing
PROJECT TITLE : Low-Rank Quaternion Approximation for Color Image Processing ABSTRACT: Grayscale image processing has seen tremendous success with methods based on low-rank matrix approximation (LRMA). By default, LRMA restores
PROJECT TITLE : Variational Bayesian Blind Color Deconvolution of Histopathological Images ABSTRACT: In most whole-slide histology images, two or more chemical dyes are used. In digital pathology, slide stain separation or colour

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry