it s a underwater image enhancement using adaptive retinal mechanism

its a project for matlab

1. KAKINADA INSTITUTE OF ENGINEERING AND TECHNOLOGY UNDERWATER IMAGE ENHANCEMENT USING ADAPTIVE RETINAL MECHANISM TEAM LEAD: T. MANOJ KUMAR REDDY TEAM MEMBERS: A.GAYATHRI K.BALA KRISHNA K.SOMASEKHAR N.PRAVEEN PRASAD GUIDE NAME: M. SURESH KUMAR SIR ASST PROF, ECE DEPT.

2. ABSTRACT The proposed approach has mainly three steps to solve the problems mentioned above. First, a simple but effective colour correction strategy is adopted to address the colour distortion. Second, a variational framework for retinex is proposed to decompose the reflectance and the illumination, which represent the detail and brightness respectively, from single underwater image. An effective alternating direction optimization strategy is adopted to solve the proposed model. Third, the reflectance and the illumination are enhanced by different strategies to address the under-exposure and fuzz problem. The final enhanced image is obtained by combining use the enhanced reflectance and illumination. OBJECTIVE main hypothesis underlying our project is that the visual systems of ocean creatures have evolved to adapt to the natural statistics of the aquatic scenes.

3. INTRODUCTION Since oceans, rivers and lakes contain abundant resources, underwater imaging has become an important researching filed and received much attention recently. While due to the absorption and scattering when light is traveling in water, there are three major problems of underwater imaging: colour distortion, under-exposure and fuzz. This degradation is mainly caused by the physical properties of the medium.

4. PROPOSED METHOD Retinex and noise aware shadow-up

5. Decomposition based on Retinal theory Retinex theory: Original image (s)= illumination (L)* reflectance (R) Need to estimate these ‘L’ and ‘R’. In general in camera representations halo artifacts occur unnaturally in the boundary of regions with large gradient values. Weighted variational model (WVM) was proposed for simultaneous reflectance and illumination estimation. Input only ‘V’ we are taking here, R=has almost no noise; L=includes noise

6. WVM S=R.L; R [0 1] L[0 INFINITE] Logarithmic approach to find R, L Log s= log R+ log L illumination is its spatial smoothness S<L ( Bcz R=point val  point val *L lesser than L) If R=1 S=L S≤L By keeping l as constant find R check output.

7. adaptive gamma correction with weighting distribution (AGCWD) Used to enhance the image value of gamma is find out automatically with the help of weighted distribution function. Data Transformation using AGCWD T (l) = cdf (l) lmax . n=0.8

8. PDF pdf= Probability distribution function (PDF) of luminance is the probability that a brightness chosen from the region is less than or equal to a given brightness value ‘a’. Pdf (l) = n1 / (M*N) n1=number of pixels that have intensity l MN =total number of pixel.

9. Shadow-up function to avoid the loss of details in bright areas due to over enhancement. Here in this process, we can take only enhanced pixel if original pixel value is greater than Ith ; else we can take direct original pixel I(x, y)∈[0,255]=intensity of illumination layer T(I( x,y ))= AGCWD output. We are taking enhanced AGCWD op iff I( x,y )< Ith I th =255-(sum(all pixel intensities)/No. of pixels)

10. YUV Output image = Enhanced illumination image *Reflectante value and V’=I′(x, y)·R(x, y). Convert into YUV Y′UV model defines a colour space in terms of one luma component (Y′) and two chrominance components, called U (blue projection) and V (red projection) respectively

11. Block-matching and 3Dfiltering (BM3D) Only ‘y’ channel is applied to BM3D to remove noise. Block-matching: used to group similar property regions. Image fragments are grouped together based on similarity. Fragments do however have the same size. A fragment is grouped if its dissimilarity with a reference fragment falls below a specified threshold. This grouping technique is called block-matching. BM3D on the other hand may group macroblocks within a single frame. All image fragments in a group are then stacked to form 3D cylinder-like shapes.

12. RESULTS

13. ADVANTAGES 1. More accurate with high reliability 2. Fast processing 3. support any type of format APPLICATIONS 1. Automobiles 2. Navy, aviation's 3. Space applications 4. Submarines

14. CONCLUSION Finally, we demonstrate the values of modelling the visual mechanisms of underwater creatures for the challenging underwater image processing task We introduced a non uniform colour correction algorithm, which could well handle the non uniform colour cast in underwater images compared to the existing methods We imitated the adaptive retinal mechanisms to control the model parameters of each low level filter according to the global contrast of a given image We exploited the colour-opponent mechanisms to flexibly adjust the colour appearance of underwater images during image enhancement. FUTURE SCOPE To enhance the visibility of image caused by atmosphere suspended particles like dust, haze and fog which causes failure in image processing such as video surveillance systems, obstacle detection systems, outdoor object recognition systems and intelligent transportation systems. And visibility restoration techniques should be developed to run under various weather conditions.