149 research outputs found

    Graph Spectral Image Processing

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    Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

    Image Compression Using Hybrid(DCT+DWT) Technique- A Comparative Study

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    When the data is in uncompressed state it becomes difficult to store and transmit it. This problem can be addressed by image compression. Image compression reduces the number of bits per pixel of the image so that its storage and transmission becomes easy. Basic goal of image compression is to increase visual quality of image with less noise. The proposed methodology satisfies this aim. Hybrid of DCT (Discrete Cosine Transformation) and DWT (Discrete Wavelet Transformation) are combined to achieve this goal. Combined advantages of DCT and DWT are used as proposed methodology. DCT has high energy compaction and less number of computational resources are required while DWT has multi resolution transformation. Hybrid DCT-DWT is proposed to compress and reconstruct images. Also colorization of the reconstructed images is proposed. Reconstructed images are stored in gray format and to visualize it colorization is done on that gray scale image. Results show that this method of compression and colorization helps in compressing and retaining color of images

    Densely-sampled light field reconstruction

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    In this chapter, we motivate the use of densely-sampled light fields as the representation which can bring the required density of light rays for the correct recreation of 3D visual cues such as focus and continuous parallax and can serve as an intermediary between light field sensing and light field display. We consider the problem of reconstructing such a representation from few camera views and approach it in a sparsification framework. More specifically, we demonstrate that the light field is well structured in the set of so-called epipolar images and can be sparsely represented by a dictionary of directional and multi-scale atoms called shearlets. We present the corresponding regularization method, along with its main algorithm and speed-accelerating modifications. Finally, we illustrate its applicability for the cases of holographic stereograms and light field compression.acceptedVersionPeer reviewe

    Image Compression by Learning to Minimize the Total Error

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    Sparse modelling of natural images and compressive sensing

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    This thesis concerns the study of the statistics of natural images and compressive sensing for two main objectives: 1) to extend our understanding of the regularities exhibited by natural images of the visual world we regularly view around us, and 2) to incorporate this knowledge into image processing applications. Previous work on image statistics has uncovered remarkable behavior of the dis tributions obtained from filtering natural images. Typically we observe high kurtosis, non-Gaussian distributions with sharp central cusps, which are called sparse in the literature. These results have become an accepted fact through empirical findings us ing zero mean filters on many different databases of natural scenes. The observations have played an important role in computational and biological applications, where re searchers have sought to understand visual processes through studying the statistical properties of the objects that are being observed. Interestingly, such results on sparse distributions also share elements with the emerging field of compressive sensing. This is a novel sampling protocol where one seeks to measure a signal in already com pressed format through randomised projections, while the recovery algorithm consists of searching for a constrained solution with the sparsest transformed coefficients. In view of prior art, we extend our knowledge of image statistics from the monochrome domain into the colour domain. We study sparse response distributions of filters constructed on colour channels and observe the regularity of the distributions across diverse datasets of natural images. Several solutions to image processing problems emerge from the incorporation of colour statistics as prior information. We give a Bayesian treatment to the problem of colorizing natural gray images, and formulate image compression schemes using elements of compressive sensing and sparsity. We also propose a denoising algorithm that utilises the sparse filter responses as a regular- isation function for the effective attenuation of Gaussian and impulse noise in images. The results emanating from this body of work illustrate how the statistics of natural images, when incorporated with Bayesian inference and sparse recovery, can have deep implications for image processing applications

    Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning

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    The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To address this concern, with the long-term goal of leveraging the abundance of cheap unlabeled data, we explore methods of unsupervised "pre-training." In particular, we propose to use self-supervised automatic image colorization. We show that traditional methods for unsupervised learning, such as layer-wise clustering or autoencoders, remain inferior to supervised pre-training. In search for an alternative, we develop a fully automatic image colorization method. Our method sets a new state-of-the-art in revitalizing old black-and-white photography, without requiring human effort or expertise. Additionally, it gives us a method for self-supervised representation learning. In order for the model to appropriately re-color a grayscale object, it must first be able to identify it. This ability, learned entirely self-supervised, can be used to improve other visual tasks, such as classification and semantic segmentation. As a future direction for self-supervision, we investigate if multiple proxy tasks can be combined to improve generalization. This turns out to be a challenging open problem. We hope that our contributions to this endeavor will provide a foundation for future efforts in making self-supervision compete with supervised pre-training.Comment: Ph.D. thesi
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