Colorization is the process of converting a black and white image - lot of different shades of gray - into a realistic colour image. Our contribution highlights the fact that the CNNs used for colorization (and specifically the data set used for their training) are not adequate to colourize legacy B&W pictures. In fact the data sets are exclusively composed of colour images, and turning these colour images into greyscale pushes the CNN to learn the contribution of each colour to the resulting luminance. Anyway, CNN are quite good in classification task and can easily recognize skies, trees and foliage, faces and persons. The colorization works well for these elements, but it fails even on memorable objects such as flags and well-known monuments albeit their representation is present in the data sets. We make improvement by using a pretrained network and add hints in the colorization process. Global hints (a colour palette given to the CNN conjointly to the image) still leave a high degree of automation, and these hints could be provided once for a collection of images related to the same theme, which is very suitable for movie colorization. Adding manual hints by scribbling colours onto the B&W image leads to even better results but needs user interaction.

Film restoration using digital image processing has been an active research field. However, the restoration of the soundtrack has been mainly performed in the sound domain, using signal processing methods, in spite of the fact that it is recorded as a continuous image between the film images and the perforations. In this paper, we present a complete system for variable area optical soundtrack restoration at the image level. The scanned soundtrack is treated using morphological operators, including a novel quasi-distance function. Working with the image representation of the soundtrack allows to take advantage of its redundancy. Moreover, with this approach only those defects that have been caused by ageing are corrected. The first results, concerning variable area soundtracks, are reported.

In this paper a new robust algorithm for global motion estimation from an MPEG stream is proposed. The approach is to use a hybrid of two previous global motion estimation algorithms in order to improve the accuracy and robustness of the estimate. The first algorithm is a non-parametric technique which uses the block based motion vectors obtained from an MPEG2 stream and which also generates a coarse background segmentation of the frame. The second algorithm is a gradient based technique which estimates the parameters from the image data directly. In this work, an initial estimate for the global motion is obtained using the non-parametric approach. The parameters are then refined using the gradient based estimation and the segmentation field. This hybrid approach shows significant reduction in mean compensation error over the non-parametric approach. The algorithm is then applied to the problem of mosaicking in sports sequences in which the global motion parameters can be used to make a panorama of an entire shot

This paper explains a method for global motion characterization using a Hough-transform-like technique to estimate the motion parameters. Motion information provided by each picture sample is gathered in an accumulator corresponding to a parameter space, which is derived from our simple motion model. The document then focuses on the use of MPEG stream as input data, dealing with the available motion compensation information as input vectors for our system. Pros, cons and improvements are discussed.

In this paper we present tests and results of an automatic color fading restoration process for digitized movies. The proposed color correction method is based on the ACE model, an unsupervised color equalization algorithm based on a perceptual approach and inspired by some mechanisms of the human visual system. This perceptual approach is local, robust and does not need any user region selection or any other user supervision. However the model has a small number of parameters that has to be set once before the filtering. The tests presented in this paper aim to study these parameters and find their effect on the final result.

A suitable detection/reconstruction approach is proposed for removing line scratches from degraded motion picture films. The detection procedure consists of two steps. First, a simple 1D-extrema detector provides line scratch candidates. Line artifacts persist across several frames. Therefore, to reject false detections, the detected scratches are tracked over the sequence using a Kalman filter. A new Bayesian restoration technique, dealing with both low and high frequencies around and inside the detected artifacts, is investigated to achieve a near invisible restoration of damaged areas.

Line scratches are common degradations in motion picture films. This paper presents an efficient method for line scratches detection strengthened by a Kalman filter. A new interpolation technique, dealing with both low and high frequencies (i.e. film grain) around the line artifacts, is investigated to achieve a nearby invisible reconstruction of damaged areas. Our line scratches detection and removal techniques have been validated on several film sequences.

This paper presents a robust technique to detect local deteriorations of old cinematographic films. This method relies on spatio-temporal information and combines two different detectors: a morphological detector which uses spatial properties of deteriorations, and a dynamic detector based on motion estimation techniques. Our deterioration detector has been validated on several film sequences and turned out to be a powerful tool for digital film restoration.

A shape recognition approach is presented. Uncertainty handling, combining, and propagation form the heart of the method. Multiple knowledge sources extract information from the segmented image and increase knowledge about undefined shapes. Knowledge sources have to be tuned to discriminate shape classes, and a critical number of independent knowledge sources guarantees the classification. Information provided by the knowledge sources is stored in the Shafer form of probability mass assignment. Dempster's rule is used to update belief in classes. A brief theoretical overview is given. Combined with a heuristic, this method achieves interesting results as well as a short execution time. An example derived from an application in the PROMETHEUS project, consisting of traffic sign recognition on a motorway, illustrates this method.>