Efficient Method For Scratch Lines Noise Removal From Video

The digitalization and transfer of older films into high definition (HD) formats imply that high quality of restoration is necessary. Now a day�s Digital film restoration is an area under discussion of increasing interest to researchers and film archives alike. Old films, including cultural heritage masterpieces, are being digitally premastered and transferred into novel, higher quality formats and distributed through various means such as DVD, Blu-ray or HD pictures. Detection of Line scratches in old movies is a particularly difficult problem due to the variable spatiotemporal characteristics of this deficiency. Some of the main problems consist of sensitivity to noise and texture, and false detections due to thin vertical structures belonging to the scene. Automatic finding of image damaged regions is the key to automatic video image in-painting. Vertical scratches are the common damages in the old film. As the film is a collection of number of frames arrayed together to produce a motion sequence hence it becomes a lengthy and tedious work to process any video format in any manner. Normally if any scratch or noise generated on films it remains as it is on many frames in sequence in film which can be benefitted by the removal process by initially checking noise area on earlier slide. Hence proposed system is aimed at designing and developing of line scratches detection from old films and remove it. A line scratches detection algorithm based on edge detection is proposed. Edge detection is nothing but an image processing technique for finding the boundaries of objects inside images. The proposed algorithm first uses the operator which has the largest response to the vertical edge in Sobel operator to detect edges, and then uses canny operator to detect edges further. Third, we detect vertical lines in the image through probabilistic Hough transform. Finally, we obtain the true locations of the vertical lines scratches through morphology and width constraints. We contribute for removal of scratches using a new nonlinear continued fraction method dealing with both spatial and temporal information around the scratch is investigated in the restoration stage

Restoration and enhancement of historical stereo photos

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+

From Grain to Pixel

Film is in a state of rapid change, with the transition from analog to digital profoundly affecting not just filmmaking and distribution, but also the theoretical conceptualization of the medium film and the practice of film archiving. New forms of digital archives are being developed that make use of participatory media to provide a more open form of access than any traditional archive has offered before. Film archives are thus faced with new questions and challenges. From Grain to Pixel attempts to bridge the fields of film archiving and academic research, by addressing the discourse on film ontology and analysing how it affects the role of film archives. Fossati proposes a new theoretization of film archival practice as the starting point for a renewed dialogue between film scholars and film archivists.Het bewegende beeld bevindt zich in een overgangsperiode waarin analoge (fotochemische) film geleidelijk vervangen wordt door digitale film. Deze overgang heeft niet alleen diepgaande invloed op filmproductie en -distributie, maar ook op de manier van archiveren van film en de theoretische conceptualisering van dit medium. Van digitale archieven worden steeds nieuwe vormen ontwikkeld. Deze archieven - digitale filmdatabases en YouTube bijvoorbeeld - maken gebruik van media die participatie van vele gebruikers mogelijk maken en worden zo toegankelijker dan ooit. Ondertussen is er nog onvoldoende dialoog tussen archivarissen en filmwetenschappers. From Grain to Pixel slaat een brug tussen archiveringspraktijken en wetenschappelijk onderzoek dat gebaseerd is op relevante debatten in film- en nieuwe mediastudies. Fossati stelt een nieuwe theorie op voor het archiveren en restaureren van film. Dit biedt mogelijkheden voor een hernieuwde dialoog tussen archivarissen en wetenschappers

From Grain to Pixel

Film is in a state of rapid change, with the transition from analog to digital profoundly affecting not just filmmaking and distribution, but also the theoretical conceptualization of the medium film and the practice of film archiving. New forms of digital archives are being developed that make use of participatory media to provide a more open form of access than any traditional archive has offered before. Film archives are thus faced with new questions and challenges. From Grain to Pixel attempts to bridge the fields of film archiving and academic research, by addressing the discourse on film ontology and analysing how it affects the role of film archives. Fossati proposes a new theoretization of film archival practice as the starting point for a renewed dialogue between film scholars and film archivists

From Grain to Pixel

"In From Grain to Pixel , Giovanna Fossati analyzes the transition from analog to digital film and its profound effects on filmmaking and film archiving. Reflecting on the theoretical conceptualization of the medium itself, Fossati poses significant questions about the status of physical film and the practice of its archival preservation, restoration and presentation. From Grain to Pixel attempts to bridge the fields of film archiving and academic research by addressing the discourse on film's ontology and analyzing how different interpretations of what film is affect the role and practices of film archives. Ultimately, Fossati proposes a novel theorization of film archival practice as the starting point for a renewed dialogue between film scholars and film archivists. Almost a decade after its first publication, this updated edition covers the latest developments in the field. Besides a new general introduction, a new conclusion and extensive updates to each chapter, a novel theoretical framework and a new case study have been added. Giovanna Fossati is chief curator at EYE Filmmuseum and professor of film heritage and digital film culture at the University of Amsterdam.

A machine-learning approach for automatic grape-bunch detection based on opponent colors

This paper presents a novel and automatic artificial-intelligence (AI) method for grape-bunch detection from RGB images. It mainly consists of a cascade of support vector machine (SVM)-based classifiers that rely on visual contrast-based features that, in turn, are defined according to grape bunch color visual perception. Due to some principles of opponent color theory and proper visual contrast measures, a precise estimate of grape bunches is achieved. Extensive experimental results show that the proposed method is able to accurately segment grapes even in uncontrolled acquisition conditions and with limited computational load. Finally, such an approach requires a very small number of training samples, making it appropriate for onsite and real-time applications that are implementable on smart devices, usable and even set up by winemakers