Abstract—Image denoising has been a well studied problem in the field of image processing and it is still a challenging problem for researches. Faster shutter speeds and higher density of image sensors (pixels) result in higher levels of noise in the captured image, which must then be processed by denoising algorithms to yield an image of acceptable quality. In this paper, we propose a method to denoise the images based on Discrete Wavelet Transform and Wavelet Decomposition using PLOW (Patch Based Locally Optimal Wiener Filter). Transformation and Decomposition provide the approximation and detailed coefficients, for reconstructed image PLOW technique is applied. The patch-based wiener filter exploits the patch redundancy for image denoising. It uses photometrically, geometrically and graphically similar patches to estimate the different filter parameters. This describes how these parameters can be accurately estimated directly from the input noisy image. The denoising framework can also be generalized to exploit such photometric redundancies within any given noisy image. Our noise removal system uses the LARK features which improve the finer estimates of pixel value and its gradients of original image. Experimental results demonstrate that our proposed study achieves good performance with respect to other denoising algorithms being compared. Experimental results are based on Peak Signal to Noise Ratio (PSNR), Mean squared error (MSE) and Structural Similarity Index Measure (SSIM).

Index Terms—patch based locally optimal wiener filter (plow), discrete wavelet transform (dwt), structural similarity index measure (ssim)

Cite: A. Gayathri, "Photometrical and Geometrical Similar Patch Based Image Denoising Using Wavelet Decomposition," Journal of Medical and Bioengineering, Vol. 4, No. 1, pp. 47-53, February 2015. Doi: 10.12720/jomb.4.1.47-53