1.To design a dictionary to attain sparse representation of signal by using K-SVD algorithm.
2.Designing a dictionary to reduce the complexity and memory requirements by using an optimized OMP method.
3.To find an alternative to patch based algorithms ,where the performance decreases as the patch size grows which can be achieved through Nystrom method.
4. To find an algorithm that works well throughout the image by preserving the details and also without artifacts by non-local means.
5.To enhance the sparse representation in transform domain by collaborative filtering to get good PSNR as well as good visual quality.
6.To denoise an image by modelling the statistical properties of an image using GSM with single step bayesian estimator.
7.The problem is to reconstruct the high frequency information of an image with non parametric statistical approach.
2.Designing a dictionary to reduce the complexity and memory requirements by using an optimized OMP method.
3.To find an alternative to patch based algorithms ,where the performance decreases as the patch size grows which can be achieved through Nystrom method.
4. To find an algorithm that works well throughout the image by preserving the details and also without artifacts by non-local means.
5.To enhance the sparse representation in transform domain by collaborative filtering to get good PSNR as well as good visual quality.
6.To denoise an image by modelling the statistical properties of an image using GSM with single step bayesian estimator.
7.The problem is to reconstruct the high frequency information of an image with non parametric statistical approach.