The document proposes a method for blind separation of image sources via adaptive dictionary learning. It aims to learn a local dictionary for each source during the separation process to improve quality, especially in noisy situations. This is an improvement over existing methods like SMICA, PCA and MMCA, which do not learn source-specific dictionaries from the mixed images.

1. BLIND SEPARATION OF
IMAGE SOURCES VIA
ADAPTIVE
DICTIONARY LEARNING
PROJECT GUIDE SUBMITTED BY
Mr.R.Devaraj B.E., D.Mohan raj
D.Raja
A.Thangapandi

2. AREA OF PROJECT
• Digital Image Processing

3. SOFTWARE REQUIRED
• MATLAB 7.14

4. ABSTRACT
• we address this problem and attempt to give a
solution via fusing the dictionary learning into the
source separation
• We first define a cost function based on this idea and
propose an extension of the denoising method in the
work of Elad and Aharon to minimize it

5. INTRODUCTION
• A local dictionary is adaptively learned for each
source along with separation.
• To improve the quality of source separation even in
noisy situations

6. EXISTING SYSTEM
• SMICA (spectral matching Independent Component
Analysis)
• PCA (Principal Component Analysis)
• MMCA(Multichannel Morphological Component
Analysis)

7. DRAWBACKS
• to learn a specific dictionary for each source
from a set of exemplar images
• no prior knowledge about the underlying
sparsity domain of the sources

8. PROPOSED SYSTEM
• To adaptively learn the dictionaries from the mixed
images within the source separation process
• Hierarchical scheme such as the one in MMCA.
ADVANTAGES
• enhances the separability of the sources.

9. BLOCK DIAGRAM

10. FUTURE ENHANCEMENT
• we further reduce the time complexity by choose the
different size of the patches