Multifocal image processing

In this paper, we present a processing method for digital images from an optical microscope. High-pass type filters are generally used for image focusing. They enhance the high spatial frequencies. These filters are not appropriate if the lack of sharpness is caused by other factors. On the other hand, the (un)sharpness can be taken as an advantage and can be used for studies of the spatial distribution of structures in the observed scene. In many cases, it is possible to construct a three- dimensional model of the observed object by analyzing image sharpness. Interesting two-dimensional images and a three-dimensional model can be obtained by applying the theory for multifocal image processing described in this paper. We improve the quality of the results compared to the previous methods using the Fourier transform for the analysis of local sharpness in the images

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

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

Image processing using miniKanren

An integral image is one of the most efficient optimization technique for image processing. However an integral image is only a special case of delayed stream or memoization. This research discusses generalizing concept of integral image optimization technique, and how to generate an integral image optimized program code automatically from abstracted image processing algorithm. In oder to abstruct algorithms, we forces to miniKanren

Processing Large Amounts of Images on Hadoop with OpenCV

Modern image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs of modern image processing algorithms. Hence, image processing often requires distributed computing. However, distributed computing is a complicated subject that demands deep technical knowledge and often cannot be used by researches who develop image processing algorithms. The framework is needed that allows the researches to concentrate on image processing tasks and hides from them the complicated details of distributed computing. In addition, the framework should provide the researches with the familiar image processing tools. The paper describes the extension to the MapReduce Image Processing (MIPr) framework that provides the ability to use OpenCV in Hadoop cluster for distributed image processing. The modified MIPr framework allows the development of image processing programs in Java using the OpenCV Java binding. The performance testing of created system on the cloud cluster demonstrated near-linear scalability

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation