![Existing method:
Such methods are known as motion compensated FRUC. If the frame rate increases in a camera
captured
video, the amount of motion blur is expected to decrease. Therefore, to obtain the best
performance in FRUC, the motion blur reduction may also be needed. However, in this paper,
we do not consider motion blur as in most existing FRUC algorithms.
Proposed method:
As a different approach in place of block matching algorithm, optical flow estimation, a pixel-based
motion estimation method first proposed by Lucas and Kanade [20], can be considered.
Several trials to solve the motion compensated FRUC problem based on optical flow estimation
have been reported. Tang and Au examined the influence of optical flow estimation on the
performance of motion compensated FRUC, and concluded that although an optical- flow-estimation-
based method can avoid blocking artifacts appearing with use of a block matching
algorithm, it can also produce salt-and-pepper artifacts [17]. The latter artifacts may be reduced
by applying a smoothness constraint to optical flow estimation. Accordingly, Krishnamurthy et
al. proposed a motion ompensated FRUC method using a smoothness-constrained optical flow
estimation approach](https://image.slidesharecdn.com/frame-rate-up-conversion-basedonvariationalimagefusion-140919024137-phpapp01/85/IEEE-2014-MATLAB-IMAGE-PROCESSING-PROJECTS-Frame-rate-up-conversion-based-on-variational-image-fusion-3-320.jpg)
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Frame rate- up- conversion- based on variational image fusion
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com Frame Rate Up Conversion Based on Variational Image Fusion Abstract—This paper presents a new framework for motion compensated frame rate up conversion (FRUC) based on variational image fusion. The proposed algorithm consists of two steps: 1) generation of multiple intermediate interpolated frames and 2) fusion of those intermediate frames. In the first step, we determine four different sets of the motion vector field using four neighboring frames. We then generate intermediate interpolated frames correspond ing to the determined four sets of the motion vector field, respectively. Multiple sets of the motion vector field are used to solve the occlusion problem in motion estimation. In the second step, the four intermediate interpolated frames are fused into a single frame via a variational image fusion process. For effective fusion, we determine fusion weights for each intermediate interpolated frame by minimizing the energy, which consists of a weighted-L1-norm based data energy and gradientdriven smoothness energy. Experimental results emonstrate that the proposed algorithm improves the performance of FRUC compared with the existing algorithms.
- 3. Existing method: Such methods are known as motion compensated FRUC. If the frame rate increases in a camera captured video, the amount of motion blur is expected to decrease. Therefore, to obtain the best performance in FRUC, the motion blur reduction may also be needed. However, in this paper, we do not consider motion blur as in most existing FRUC algorithms. Proposed method: As a different approach in place of block matching algorithm, optical flow estimation, a pixel-based motion estimation method first proposed by Lucas and Kanade [20], can be considered. Several trials to solve the motion compensated FRUC problem based on optical flow estimation have been reported. Tang and Au examined the influence of optical flow estimation on the performance of motion compensated FRUC, and concluded that although an optical- flow-estimation- based method can avoid blocking artifacts appearing with use of a block matching algorithm, it can also produce salt-and-pepper artifacts [17]. The latter artifacts may be reduced by applying a smoothness constraint to optical flow estimation. Accordingly, Krishnamurthy et al. proposed a motion ompensated FRUC method using a smoothness-constrained optical flow estimation approach
- 4. Merits: 1. Better PSNR values 2. Output image more enhancement. 3. Low BER rate Demerits: 1.noise level is very high 2. restoration process time is very high.
- 5. Results:
- 7. Existing method: Both the spatial layout of locally and globally distributed regions in a scene play important roles in determining photo aesthetics. As seen from the spatial interaction of the four linearly arranged sailboats captures the regional aesthetics; while the relative displacement of the sailboats, the water, and the sky reflects the global aesthetics. Existing rules can only heuristically define what spatial distribution among the salient image regions is aesthetically pleasing. For example, rule of the thirds favors salient regions locating near the evenly 3 × 3 intersections of a photo. Proposed method: Recently many photo aesthetics evaluation methods have been proposed. Roughly, these methods can be divided into two groups: global feature-based approaches and local patch integration-based approaches. Global feature-based approaches design global low- level and high- level visual features to represent photo aesthetics in an implicit manner. Ke et al. designed a group of high- level visual features, such as the image simplicity based on the spatial distribution of edges, to imitate human perception of photo aesthetic quality.
- 8. Merits: 4. Better PSNR values 5. Output image more enhancement. 6. Low BER rate Demerits: 1.noise level is very high 2. restoration process time is very high.
- 9. Results: