Team 5 Wavelets for Image Fusion Xiaofeng “Sam” Fan Jiangtao “Willy” Kuang Jason “Jingsu” West

Scope  Problem Definition  Wavelet Transforms  Fusion Rules  Approach

Problem Definition  Implement a comparison scheme to enable qualitative and quantitative analysis of wavelet transform and fusion rule combinations.  Then, with gained knowledge and experience, extend work to novel area.

 Introduction Based on small waves, with varying frequency and limited duration.  Multiresolution Focus on the representation and analysis of images at more than one resolution Wavelet Transforms

What’s the advantage

What’s the advantage (con)

2D DWT T = HFH F is input image matrix. H is the transform matrix.

Wavelet Feature

Shift Invariant Wavelet Transform There are a number of possible solutions to the shift variance problem. Most of them fall into two parts: a) Limit the sub-band subsampling (CWT, PSDWT) b) Minimising shift variance is to build two wavelet decomposition trees. b) Minimising shift variance is to build two wavelet decomposition trees.

Image Fusion Schemes

Or Coefficient-based Activity(CBA) – absolute value or square of coefficient in the MSD representation: A: Weighted average method (WA-WBA): B: Rank filter method (RF-WBA): RBA: Edge detector – region segmentation – labeling algorithm Activity Level Measurement

Coefficient Grouping Method Coefficients relationship among other frequency bands and other decomposition levels NG: Not associated with each other SG: In the same decomposition scale MG: All the corresponding MSD samples together

Coefficient Combining Method No Yes Combine the source MSD representations to produce composite MSD representation

Consistency Verification Considering about the neighboring coefficients in the composite MSD coefficient No consistency verification WBV: use a majority filter in a 3x3 or 5x5 windows used for the neighborhood. RBV: an edge image and a region image are first generated; for region samples we apply the majority filter over the region; for edge, we apply a constraint majority filter – the center sample will not be changed unless it is different from all the neighboring samples

Experimental Fusion schemes  CBA NG CM NV  WA-WBA SG WA WBV  RF-WBA (MG) (RBV)  (RBA) Activity MeasureGroupingCombiningVerification

Approach  Four Phase Implementation Process  Generation of test images  Constructing Test Architecture  Implementing WT/Fusion Rules  Analysis/Evaluation of Results  Literature Search, Presentations, and Documentation also included

Schedule Week # Main Tasks Deadlines Week 1 Meet and Organize Week 2 Literature Search / Brainstorm Topic Topic Presentation Week 3 Lit Search / Define Problem & Approach Approach Presentation Week 4 Implement Wavelets and Rules Literature Review Write Up Week 5 Implement Wavelets and Rules Week 6 Complete Implementation Progress Presentation Week 7 Analysis & Evaluation of Results Implementation Write Up Week 8 Extension of Work Week 9 Extension of Work Week 10 Finish Extension and Analysis Final Document & Presentation

Test Images  Test images may be from pictorial, medical, or remote sensing applications

Smoothed Test Images  Gray scale images with different blurred regions

Other Distortions  Varying degree of smoothing  Addition of noise  Rotation or Misregistration

Result Analysis/Evaluation  Qualitative – visual comparison as a method of cursory evaluation  Quantitative – Quality metric for determining accuracy of process

Extension of Work  New topics arise as we implement the comparison scheme  Possible topics thus far:  Applications of fusion in color images  Image screening to enable automated image fusion by best determined method

Summary  Problem has been defined  Solid background on wavelets and fusion  Approach is in place  Schedule has been set  Some test images have been generated  Brainstorming continues on novel extension