Blurred image recognization system
- 1. BLURRED IMAGE RECOGNITION PRESENTED BY, RAJESWARI PRAVIN KUMAR B.E , M.TECH..
- 2. OBJECTIVES: - The main objective of this project is to recognize the blurred image - Blurred image recognition is used for restorage purpose - Applicable in automatic target recognition & tracking, character recognition, 3D scene analysis & reconstruction.
- 3. EXISTING SYSTEM: - Blurred image recognition by complex moment invariants, this is existing system , blurred image was recognized by using the complex moments . - Complex moments are with respect to centrally symmetric blur, this does not provide the recognition accuracy & also it is sensitive to noise ,this is due to the fact that the polynomials are not orthogonal.
- 4. PROPOSED SYSTEM: - The proposed system is blurred image recognition by using orthogonal moments . - The orthogonal moments are better than the other types of moments in terms of information redundancy & are most robust to noise. - The performance of the proposed descriptors is evaluated with various point spread functions and different image noises. - The proposed descriptors are more robust to noise & have better discriminative power than the methods based on complex moments
- 5. INTRODUCTION: - One of the most frequent tasks in image processing is the recognition of an image (or, more frequently, of an object on the image) against images stored in a database. - Whereas the images in the database are supposed to be ideal, the acquired image represents the scene mostly in an unsatisfactory manner. - Because real imaging systems as well as imaging conditions are imperfect, an observed image represents only a degraded version of the original scene.
- 6. CONT… - Blur is introduced into the captured image during the imaging process by such factors as diffraction, lens aberration, wrong focus, and atmospheric turbulence. - The widely accepted standard linear model describes the imaging process by a convolution of an unknown original (or ideal) image f ( z , y ) with a space-invariant point spread function (PSF) h(x, Y) - where g(z,y) represents the observed image. The PSF h ( z , y )describes the imaging system, and in our case, it is supposed to be unknown.
- 7. Steps to recognize & reconstruct: Input image Moments invariants Edge detection Mask creation Output image
- 8. CONT.., INPUT IMAGE: - Image is captured through the camera , if that image is in unsatisfactory manner means known as blurred image - The images are affected because of the following factors, 1. Wrong focusing 2. Atmospheric turbulence 3. Lens aberration
- 9. Cont.., - There are different types blurred images , some of them are, - Zoom Blur - Motion Blur - Atmospheric Blur - Domain Shifting - Threshold Blur
- 10. Cont.., ZOOM BLUR: - This type of image is created due to long focusing of the camera lens i.e out of focusing the image
- 11. Blurred images : Out of focus
- 12. MOTION BLUR : - This type of image is created due to Direction change in the real image sensing system (camera)
- 13. ATMOSPHERIC BLUR: - This type of image is created due to varies atmospheric changes
- 14. DOMAIN SHIFTING: - This type of image is created due to varies shifting in the image
- 15. ADD NOISE TO AN IMAGE: - Varies noises are , - White Gaussian noise - Salt & pepper noise - Noises are added , because it only gives recognization process. - From that, define the filter co- efficient
- 16. Blurred images: corrupted by various types of noise
- 17. Cont.., LEGENDRE MOMENTS: - The blurred image is recognized by using the legendre moments invariants - Orthogonal moments are mainly used to recognize the blurred image - Orthogonal moments cover the whole image during the recognization process
- 18. CONT.., BLUR INVARIANTS: - The blurred image is compared with the database , by using the orthogonal moments - Blur are some type of noises( gaussian noise with standard deviation and salt & pepper noise) - Here , calculate the point spread function for deblurring the image i.e calculate the blur invariants
- 19. EDGE DETECTION: - It function is mainly detect the edges of an image - Edges are used to reconstruct the image
- 20. MASK CREACTION : - Mask Creation is based upon the PSF values i.e filter values - Apply the convolution between the original image with the image prior , from that deblur the image
- 21. Cont., RECONSTRUCTED IMAGE: - Finally , the original image is reconstructed by using this moments invariants method - This will provide the greatest accuracy compared with the previous method
- 22. Blurred image is compared with original image:
- 23. SOFTWARE DETAILS: - MATLAB 7.8
- 24. START Read an image from workspace Add noise to an image Choose the noise to be added Choose the noise if = 1 Apply White Gaussian noise Display the image A FLOW CHART:
- 25. if = 2 Apply salt & pepper noise Display the image If = 3 Noise free Display the image If > 3 Terminate B A
- 26. Find the blur invariants Perform the edge detection Load filter values Create the mask Apply convolution between unknown image with blurred image Reconstructed image B
- 29. ImagePrior
- 31. APPLICATIONS: - Image Security - 3-D Scence analysis & reconstruction - Automatic recognization & tracking - Restorage purpose
- 32. THANK YOU