The Atomic Force Microscope (AFM) allows the measurement of interactions at interfaces with nanoscale resolution. Imperfections in the shape of the tip often lead to the presence of imaging artefacts such as the blurring and repetition of objects within images. Generally, these artefacts can only be avoided by discarding data and replacing the probe. Under certain circumstances (e.g., rare, high value samples, or extensive chemical/physical tip modification) such an approach is not feasible. Here, we apply a novel deblurring technique, using a Bayesian framework, to yield a reliable estimation of the real surface topography without any prior knowledge of the tip geometry (blind reconstruction). A key contribution is to leverage the significant recently successful body of work in natural image deblurring to solve this problem. We focus specifically on the 'double-tip' effect, where two asperities 1 are present on the tip, each contributing to the image formation mechanism. Finally, we demonstrate that the proposed technique successfully removes the 'double-tip' effect from high resolution AFM images which demonstrate this artefact whilst preserving feature resolution.

This paper introduces a method for registering scans acquired by Atomic Force Microscopy (AFM). Due to compromises between scan size, resolution, and scan rate, high resolution data is only attainable in a very limited field of view. The proposed method uses a sparse set of feature matches between the low and high resolution AFM scans and maps them onto a common coordinate system. This can provide a wider field of view of the sample and give context to the regions where high resolution AFM data has been obtained. The algorithm employs a robust approach overcoming complications due to temporal sample changes and sample drift of the AFM system which becomes significant at higher-resolutions. To our knowledge, this is the first approach for automatic high resolution AFM image registration. Experimental results show the correctness and robustness of our approach and shows that the estimated transforms can be used to deduce plausible measures of sample drift.

This paper presents algorithms for the digital restoration of films damaged by tear. As well as causing local image data loss, a tear results in a noticeable relative shift in the frame between the regions at either side of the tear boundary. This paper describes a method for delineating the tear boundary and for correcting the displacement. This is achieved using a graph-cut segmentation framework that can be either automatic or interactive when automatic segmentation is not possible. Using temporal intensity differences to form the boundary conditions for the segmentation facilitates the robust division of the frame. The resulting segmentation map is used to calculate and correct the relative displacement using a global-motion estimation approach based on motion histograms. A high-quality restoration is obtained when a suitable missing-data treatment algorithm is used to recover any missing pixel intensities.

Film Tear is a form of degradation in archived film and is the physical ripping of the film material. Tear causes displacement of a region of the degraded frame and the loss of image data along the boundary of tear. In [1], a restoration algorithm was proposed to correct the displacement in the frame introduced by the tear by estimating the global motion of the 2 regions either side of the tear. However, the algorithm depended on a user-defined segmentation to divide the frame. This paper presents a new fully-automated segmentation algorithm which divides affected frames along the tear. The algorithm employs the graph cuts optimisation technique and uses temporal intensity differences, rather than spatial gradient, to describe the boundary properties of the segmentation. Segmentations produced with the proposed algorithm agree well with the perceived correct segmentation.

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 outlines an algorithm to improve the robustness of missing data treatment to pathological motion (PM). PM can cause misdiagnosis of clean image data as missing data. The proposed algorithm uses a probabilistic framework to jointly detect PM and missing data by exploiting more temporal information than is typically used for missing data detection and by exploiting the local smoothness assumption of motion fields. The results of the framework are compared to an equivalent missing data detector without PM detection and the framework is shown to prevent the misdiagnosis of missing data due to PM.