67 research outputs found

    Multi-Directional Scratch Detection and Restoration in Digitized Images

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    Line scratches are common defects in old archived videos, but similar imperfections may occur in printed images, in most cases by reason of improper handling or inaccurate preservation of the support. Once an image is digitized, its defects become part of that image. Many state-of-the-art papers deal with long, thin, vertical lines in old movie frames, by exploiting both spatial and temporal information. In this paper we aim to face with a more challenging and general problem: the analysis of line scratches in still images, regardless of their orientation, color, and shape. We present a detection/restoration method to process this defect

    Innovative Techniques for Digitizing and Restoring Deteriorated Historical Documents

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    Recent large-scale document digitization initiatives have created new modes of access to modern library collections with the development of new hardware and software technologies. Most commonly, these digitization projects focus on accurately scanning bound texts, some reaching an efficiency of more than one million volumes per year. While vast digital collections are changing the way users access texts, current scanning paradigms can not handle many non-standard materials. Documentation forms such as manuscripts, scrolls, codices, deteriorated film, epigraphy, and rock art all hold a wealth of human knowledge in physical forms not accessible by standard book scanning technologies. This great omission motivates the development of new technology, presented by this thesis, that is not-only effective with deteriorated bound works, damaged manuscripts, and disintegrating photonegatives but also easily utilized by non-technical staff. First, a novel point light source calibration technique is presented that can be performed by library staff. Then, a photometric correction technique which uses known illumination and surface properties to remove shading distortions in deteriorated document images can be automatically applied. To complete the restoration process, a geometric correction is applied. Also unique to this work is the development of an image-based uncalibrated document scanner that utilizes the transmissivity of document substrates. This scanner extracts intrinsic document color information from one or both sides of a document. Simultaneously, the document shape is estimated to obtain distortion information. Lastly, this thesis provides a restoration framework for damaged photographic negatives that corrects photometric and geometric distortions. Current restoration techniques for the discussed form of negatives require physical manipulation to the photograph. The novel acquisition and restoration system presented here provides the first known solution to digitize and restore deteriorated photographic negatives without damaging the original negative in any way. This thesis work develops new methods of document scanning and restoration suitable for wide-scale deployment. By creating easy to access technologies, library staff can implement their own scanning initiatives and large-scale scanning projects can expand their current document-sets

    Patch-based graphical models for image restoration

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    Mathematical Approaches for Image Enhancement Problems

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    This thesis develops novel techniques that can solve some image enhancement problems using theoretically and technically proven and very useful mathematical tools to image processing such as wavelet transforms, partial differential equations, and variational models. Three subtopics are mainly covered. First, color image denoising framework is introduced to achieve high quality denoising results by considering correlations between color components while existing denoising approaches can be plugged in flexibly. Second, a new and efficient framework for image contrast and color enhancement in the compressed wavelet domain is proposed. The proposed approach is capable of enhancing both global and local contrast and brightness as well as preserving color consistency. The framework does not require inverse transform for image enhancement since linear scale factors are directly applied to both scaling and wavelet coefficients in the compressed domain, which results in high computational efficiency. Also contaminated noise in the image can be efficiently reduced by introducing wavelet shrinkage terms adaptively in different scales. The proposed method is able to enhance a wavelet-coded image computationally efficiently with high image quality and less noise or other artifact. The experimental results show that the proposed method produces encouraging results both visually and numerically compared to some existing approaches. Finally, image inpainting problem is discussed. Literature review, psychological analysis, and challenges on image inpainting problem and related topics are described. An inpainting algorithm using energy minimization and texture mapping is proposed. Mumford-Shah energy minimization model detects and preserves edges in the inpainting domain by detecting both the main structure and the detailed edges. This approach utilizes faster hierarchical level set method and guarantees convergence independent of initial conditions. The estimated segmentation results in the inpainting domain are stored in segmentation map, which is referred by a texture mapping algorithm for filling textured regions. We also propose an inpainting algorithm using wavelet transform that can expect better global structure estimation of the unknown region in addition to shape and texture properties since wavelet transforms have been used for various image analysis problems due to its nice multi-resolution properties and decoupling characteristics

    Restoration and enhancement of historical stereo photos

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    Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed, referred to as Stacked Median Restoration plus (SMR+). The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished in three steps: (1) image fusion according to the stacked median operator, (2) low-resolution detail enhancement by guided supersampling, and (3) iterative visual consistency checking and refinement. Each step implements an original algorithm specifically designed for this work. The restored image is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods. Results also show that the performance of the state-of-the-art single-image deep restoration network Bringing Old Photo Back to Life (BOPBtL) can be strongly improved when the input image is pre-processed by SMR+

    Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs

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    SIAM Journal Multiscale Modeling & SimulationTo develop better image change detection algorithms, new models able to capture spatio-temporal regularities and geometries present in an image pair are needed. In this paper, we propose a multiscale formulation for modeling semi-local inter-image interactions and detecting local or regional changes in an image pair. By introducing dissimilarity measures to compare patches and binary local decisions, we design collaborative decision rules that use the total number of detections obtained from the neighboring pixels, for different patch sizes. We study the statistical properties of the non-parametric detection approach that guarantees small probabilities of false alarms. Experimental results on several applications demonstrate that the detection algorithm (with no optical flow computation) performs well at detecting occlusions and meaningful changes for a variety of illumination conditions and signal-to-noise ratios. The number of control parameters of the algorithm is small and the adjustment is intuitive in most cases

    Understanding and advancing PDE-based image compression

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    This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf

    Computer aided puzzle assembly based on shape and texture information /

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    Puzzle assembly’s importance lies into application in many areas such as restoration and reconstruction of archeological findings, the repairing of broken objects, solving of the jigsaw type puzzles, molecular docking problem, etc. Puzzle pieces usually include not only geometrical shape information but also visual information of texture, color, continuity of lines, and so on. Moreover, textural information is mainly used to assembly pieces in some cases, such as classic jigsaw puzzles. This research presents a new approach in that pictorial assembly, in contrast to previous curve matching methods, uses texture information as well as geometric shape. The assembly in this study is performed using textural features and geometrical constraints. First, the texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The feature values are derived by these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. Two new algorithms using Fourier based image registration techniques are developed to optimize the affinity. The algorithms for inpainting, affinity and Fourier based assembly are explained with experimental results on real and artificial data. The main contributions of this research are: The development of a performance measure that indicates the level of success of assembly of pieces based on textural features and geometrical shape. Solution of the assembly problem by using of the Fourier based methods
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