CHAPTER 7. Page No. 7 Conclusions and Future Scope Conclusions Future Scope 123

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1 CHAPTER 7 Page No 7 Conclusions and Future Scope Conclusions Future Scope 123

2 121 CHAPTER 7 CONCLUSIONS AND FUTURE SCOPE 7.1 CONCLUSIONS In this thesis, the investigator discussed mainly about two important image processing tasks namely image denoising and image compression. In chapter 4 image denoising using spatial filtering, transform domain filtering is explained. Gaussian noise has been added to the original image to implement different algorithms. In spatial filtering, average filter, median filter and wiener filter with different mask sizes are considered for different levels of standard deviation. From the experimental analysis, it is evident that wiener filter performs better than other filters if the size of filter mask increases. In transform domain, the investigator applied a multi scale, multi resolution, multi orientation and invertible subband representation of wavelet transform for decomposing the image into low frequency approximation coefficients and high frequency detailed coefficients. High frequency detailed coefficients related to image noise can be eliminated by using different threshold methods. The tradeoff between noise elimination and detail preservation is analyzed using some of the performance measures like mean square error, root mean square error values, peak signal to noise ratio and human visual system. Discrete Wavelet Transform (DWT) based denoising is implemented with different thresholds like hard, soft and Bishrink methods. Here different wavelet families like daubechies wavelet family and hybrid wavelets are considered and observed that in daubechies, db6 filters are giving better results compared to others. While using Hybrid wavelets, the algorithm is performing better with

3 122 Coiflets and Symlets. The effectiveness of each wavelet filter is dependent on the amount of noise included in the image. In the same transform domain another algorithm has been implemented based on Diversity Enhanced Discrete Wavelet Transform (DEDWT). Here DEDWT is implemented along with Soft Thresholding, Hard Thresholding and Bishrink method with similar wavelets and Hybrid wavelets. From the results, it is observed that DEDWT along with Hybrid wavelets method outperforms compared to other methods. A comparison between the performance of various wavelet functions is explained. The effectiveness is dependent upon the type of the image and the amount of noise contamination. Finally, another algorithm in image denoising is implemented by using Complex Wavelet Transform. Here 2D-DWT, 2D Dual Tree Real and Complex Wavelet Transform based denoising is analyzed on a standard Lena image and found that the results obtained by using 2D DT-CWT are superior to other methods. In chapter 5, Hyperanalytic Wavelet Transform(HWT) based image denoising is discussed. The advantage of using HWT is, any wavelet family can be used to enhance the directionality over 2D-DT-CWT. This algorithm is implemented on standard images along with wiener filter and compared with the spatial wiener method and DWT based wiener filtering method. From the tabulations, it can be easily judged that there is an improvement in quality of the denoised image, if the size of the input image is increased spatially. In Chapter 6, the investigator analyzed the usage of complex wavelet transform in another important application of image processing i.e image compression. Here the investigator proposed a 2D-DT-CWT based image compression algorithm along with huffman coding. In DWT coefficients, the most

4 123 significant information show high amplitudes and less significant information show low amplitudes. These low amplitude wavelet coefficients are applied to thresholding depending upon the lossy compression or lossless compression. By neglecting these coefficients, compression can be improved. The algorithm is examined on gray colour image, medical image and colour images. Different parameters like original image size, compressed image size, compression ratio(cr), bits per pixel(bpp), PSNR and RMS error for various thresholds have been tabulated. From the results, it is observed that the proposed 2D- DT-CWT algorithm is giving consistent results compared to the existing method. It is also identified that the results obtained by proposed method are as good as the compression method using embedded zero-tree wavelet (EZW) and huffman coding. Finally, it is concluded that the investigational results obtained by using complex wavelet transform methods exhibit perfection in precision over the standard wavelet methods. The enhancement in the results can be noticed qualitatively and quantitatively. The two main reasons for generating better results with complex wavelet transforms are directionality and improvement in shift invariance. 7.2 FUTURE SCOPE The work presented in this thesis can be extended in several directions. The denoising algorithms can be extended for analyzing the images affected with salt and pepper noise, speckle noise and other noise models. The advantage of the DWT is in its flexibility caused by the choice of various wavelet functions improves PSNR values and produce visually pleasing images. The algorithms can be extended in such a way that the way of reducing the noise from an image without losing the actual and important information. A thorough research can be done in choosing a mother wavelet suitable for denoising a particular image.

5 124 In this thesis the investigator showed the usage of complex wavelets in image processing tasks such as image denoising and image compression. From the investigation results it is evident that complex wavelet based image analysis perform better than the standard wavelet transforms. Hence, 2D-DT-CWT and HWT can be applied to different image processing tasks such as image topology, image watermarking, denoising of magnetic resonance images, image segmentation, image inpainting, real time audio and video compression applications. Some image processing applications require an accurate determination of object boundary. In future research, the present algorithms can be extended with the help of morphological operations to extract the boundary of the medical objects which in turn useful in image topology. Implementation of HWT can be improved by reducing the approximation errors obtained with Hilbert Transform. A thorough research can be done to extend HWT to Diversity Enhanced HWT (DEHWT).