Progressive Furniture Model Decimation with Texture Preservation

We develop an efficient MRI denoising algorithm based on sparse representation and curvelet transform with variance stabilizing transformation framework. By using sparse representation, a MR image is decomposed into a sparsest coefficients matrix with more no of zeros. Curvelet transform is directional in nature and it preserves the important edge and texture details of MR images. In order to get sparsity and texture preservation, we post process the denoising result of sparse based method through curvelet transform. To use our proposed sparse based curvelet transform denoising method to remove rician noise in MR images, we use forward and inverse variance-stabilizing transformations. Experimental results reveal the efficacy of our approach to rician noise removal while well preserving the image details. Our proposed method shows improved performance over the existing denoising methods in terms of PSNR and SSIM for T1, T2 weighted MR images.

Download Full-text

A Novel Method of Image Denoising: New Variant of Block Matching and 3D

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3185 ◽

2020 ◽

Vol 10 (10) ◽

pp. 2490-2500

Author(s):

Sadaf Zahid Mahmood ◽

Humaira Afzal ◽

Muhammad Rafiq Mufti ◽

Nadeem Akhtar ◽

Asad Habib ◽

...

Keyword(s):

Image Denoising ◽

Adaptive Filter ◽

Distance Measure ◽

Signal To Noise Ratio ◽

Visual Image ◽

Block Matching ◽

Experimental Result ◽

New Variant ◽

Novel Method ◽

Texture Preservation

The demand of accurate and visually fair images is increasing with the passage of time and bang of the number of digital images especially in the domain of medical and healthcare systems. The visual image quality of modern cameras affected due to edges, textures and sharp structures noise. Though research community has introduced several techniques such as BM3D (Block Matching and 3D) for image denoising. However, edges and texture preservation capabilities remain issues due to hard thresholds values and captured image diversity. In order to address these issues, we propose a new variant of BM3D namely BM3DMA (Block Matching and 3D with Mahalanobis and Adaptive filter) which is employed through the use of Mahalanobis distance measure (for diversity coverage) and adaptive filter (for soft thresholds). We used two widely known datasets consist of set of standard and medical images. We observe 5% to 10% enhancement in the performance of BM3DMA as compared to BM3D in terms of improving the PSNR (Peak Signal to Noise Ratio) value. The promising experimental result indicates the effectiveness of BM3DMA in terms preserving the edge and texture image noise.

Download Full-text

Image Restoration Using Adaptive Region-Wise p-Norm Filter with Local Constraints

International Journal of Image and Graphics ◽

10.1142/s021946781650008x ◽

2016 ◽

Vol 16 (02) ◽

pp. 1650008

Author(s):

A. A. Bini ◽

P. Jidesh

Keyword(s):

Image Restoration ◽

Regularization Parameter ◽

Image Features ◽

Local Constraints ◽

Mean Curvature Motion ◽

Statistical Measures ◽

Image Characteristics ◽

Curvature Motion ◽

Laplacian Filter ◽

Texture Preservation

In this work, we introduce a feature adaptive second-order p-norm filter with local constraints for image restoration and texture preservation. The p-norm value of the filter is chosen adaptively between 1 and 2 in a local region based on the regional image characteristics. The filter behaves like a mean curvature motion (MCM) [A. Marquina and S. Osher, SIAM Journal of Scientific Computing 22, 387–405 (2000)] in the regions where the p-norm value is 1 and switches to a Laplacian filter in the rest of the regions (where the p-norm value is 2). The proposed study considerably reduces stair-case effect and effectively removes noise from images while deblurring them. The noise is assumed as Gaussian distributed (with zero mean and variance [Formula: see text]) and blur is linearly shift invariant (out-of-focus). The filter converges at a faster rate with semi-implicit Crank–Nicholson scheme. The regularization parameter is initialized and updated based on the local image features and therefore this filter preserves edges, structures, textures and fine details present in images very well. The method is applied on different kinds of images with different image characteristics. We show the response of the filter to various kinds of images and numerically quantify the performance in terms of standard statistical measures.

Download Full-text