UNSUPERVISED CONVOLUTIONAL NEURAL NETWORKS FOR MOTION ESTIMATION

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.Traditional methods for motion estimation estimate the motion field F between a pair of images as the one that minimizes a predesigned cost function. In this paper, we propose a direct method and train a Convolutional Neural Network (CNN) that when, at test time, is given a pair of images as input it produces a dense motion field F at its output layer. In the absence of large datasets with ground truth motion that would allow classical supervised training, we propose to train the network in an unsupervised manner. The proposed cost function that is optimized during training, is based on the classical optical flow constraint. The latter is differentiable with respect to the motion field and, therefore, allows backpropagation of the error to previous layers of the network. Our method is tested on both synthetic and real image sequences and performs similarly to the state-of-the-art methods

Improved Wyner-Ziv video coding efficiency using bit plane prediction

The research work is partially funded by STEPS-Malta and partially by the European Union - ESF 1.25.Distributed Video Coding (DVC) is a coding paradigm where video statistics are exploited, partially or totally, at the decoder. The performance of such a codec depends on the accuracy of the soft-input information estimated at the decoder, which is affected by the quality of the side information (SI) and the dependency model. This paper studies the discrepancies between the bit planes of the Wyner-Ziv (WZ) frames and the corresponding bit planes of the SI. The relationship between these discrepancies is then exploited to predict the locations where the bit plane of the SI is expected to differ from that of the original WZ frame. This information is then used to derive more accurate soft-input values that achieve better compression efficiencies. Simulation results demonstrate that a WZ bit-rate reduction of 9.4% is achieved for a given video quality.peer-reviewe

Generalized Inpainting Method for Hyperspectral Image Acquisition

A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters. Such an agile imaging technique comes at the cost of a reduced spatial resolution and the need for a demosaicing procedure on its interleaved data. In this work, we address both issues and propose an approach inspired by recent developments in compressed sensing and analysis sparse models. We formulate our superresolution and demosaicing task as a 3-D generalized inpainting problem. Interestingly, the target spatial resolution can be adjusted for mitigating the compression level of our sensing. The reconstruction procedure uses a fast greedy method called Pseudo-inverse IHT. We also show on simulations that a random arrangement of the spectral filters on the sensor is preferable to regular mosaic layout as it improves the quality of the reconstruction. The efficiency of our technique is demonstrated through numerical experiments on both synthetic and real data as acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding, sparse models, CMOS, Fabry-P\'ero

Performance of enhanced error concealment techniques in multi-view video coding systems

This research work is partially funded by the Strategic Educational Pathways Scholarship Scheme (STEPS-Malta). This scholarship is partly financed by the European Union - European Social Fund (ESF 1.25).Transmission of multi-view video encoded bit-streams over error-prone channels demands robust error concealment techniques. This paper studies the performance of solutions that exploit the neighbourhood spatial, temporal and inter-view information for this scope. Furthermore, different boundary distortion measurements, motion compensation refinement and temporal error concealment of Anchor frames were exploited to improve the results obtained by the basic error concealment techniques. Results show that a gain in performance is obtained with the implementation of each independent concealment technique. Furthermore, Peak Signal-to-Noise Ratio (PSNR) gains of about 4dB relative to the standard were achieved when adopting a hybrid error concealment approach.peer-reviewe

From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imaging, image analysis, and image-guided treatment, alongside physiological signal processing, computational biology, and bioinformatics. BISP has 40 members and covers a wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing, BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP plays a central role in the organization of the IEEE International Symposium on Biomedical Imaging (ISBI) and contributes to the technical sessions at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), and the IEEE International Conference on Image Processing (ICIP). In this paper, we provide a brief history of the TC, review the technological and methodological contributions its community delivered, and highlight promising new directions we anticipate