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Rajwade, Ajit

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Ajit Rajwade

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2013 - 20153

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    Rough set based image denoising for brain MR images
    (Elsevier, 01-10-2014) Phophalia, Ashish; Rajwade, Ajit; Mitra, Suman; DA-IICT, Gandhinagar; Phophalia, Ashish (201021014)
    In this paper, we propose a novel approach to explore self-similarity of an image for patch based image processing application. The motivation of this work is to search for a similar set of pixels from a given image for each pixel or patch present in the image. So far, the search for similarity exploration in the image is a time consuming task and restricted to a local search space in many of the previous works. The proposed method explores the image space globally for each given patch using Rough Set Theory (RST) in a principled way. The similarity in the image space is explored according to a predefined set of attribute(s) of the image. The selection strategy using RST has been applied for an image denoising task to enhance the capability of the underlying method. We have demonstrated the suitability of RST for a similar patch selection applying it on two state-of-the-art methods and hence proposed a new algorithm in comparison to the state-of-the-art methods that is efficient in terms of computational complexity. The applicability of denoising methods has been shown on the medical image domain and evaluated quantitatively using various statistical measures. The performance of proposed method was found to be comparable and satisfactory.
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    Image Denoising Using the Higher Order Singular Value Decomposition
    (IEEE, 01-04-2013) Rajwade, Ajit; Rangarajan, Anand; Banerjee, Arunava; DA-IICT, Gandhinagar
    In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented
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    Image denoising using orthogonal locality preserving projections
    (SPIE, 01-08-2015) Shikkenawis, Gitam; Mitra, Suman; Rajwade, Ajit; DA-IICT, Gandhinagar; Shikkenawis, Gitam (201221004)
    Image denoising approaches that learn spatially adaptive dictionaries from the observed noisy image have gathered a lot of attention in the past decade. These methods rely on the hypothesis that patches from the underlying clean image can be expressed as sparse linear combinations of these dictionary vectors (bases). We present a framework for inferring an orthonormal set of dictionary vectors using orthogonal locality preserving projection (OLPP). This ensures that patches that are similar in the noisy image should produce similar coefficients when projected in the OLPP domain. Unlike other projection methods, the locality preserving property of OLPP automatically groups similar patches together during inference of the basis. Hence, only one global orthonormal basis suffices to sparsely represent patches from a large subimage or a large portion of the image. The proposed amalgamation of the sparsity and global dictionary make the current approach more suitable for an image denoising task with reduced computational complexity. Experiments on several benchmark datasets made it clear that the proposed method is capable of preserving fine textures while denoising an image, on par with or surpassing several state-of-the-art methods for gray-scale and color images.