Sorry, you need to enable JavaScript to visit this website.

We consider the problem of super-resolution for sub-diffraction imaging. We adapt conventional Fourier ptychographic approaches, for the case where the images to be acquired have an underlying structured sparsity. We propose some sub-sampling strategies which can be easily adapted to existing ptychographic setups. We then use a novel technique called CoPRAM with some modifications, to recover sparse (and block sparse) images from sub-sampled ptychographic measurements.

Single-image blind deblurring is a challenging ill-posed in- verse problem which aims to estimate both blur kernel and latent sharp image from only one observation. This paper fo- cuses on first estimating the blur kernel alone and then restor- ing the latent image since it has been proven to be more feasi- ble to handle the ill-posed nature during blind deblurring. To estimate an accurate blur kernel, L0-norm of both first- and second-order image gradients is proposed to regularize the final estimation result.

Storage, browsing and analysis of human activity videos can be significantly facilitated by automated video summarization. Unsupervised key-frame extraction remains the most widely applicable technique for summarizing activity videos. However, their specific properties make the problem difficult to solve. Typical relevant algorithms fall under the video frame clustering or the dictionary-of-representatives families, with salient dictionary learning having been recently proposed.

In this work we propose tracking as a generic addition to the instance search task. From video data perspective, much information that can be used is not taken into account in the traditional instance search approach. This work aims to provide insights on exploiting such existing information by means of tracking and the proper combination of the results, independently of the instance search system. We also present a study on the improvement of the system when using multiple independent instances (up to 4) of the same person

In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of highly ranked images in a topological space, assuming that false positives reside on different manifolds from the query. For this purpose, a mutual rank graph is defined on pseudo-relevant images, and their distribution is learned by diffusing their query similarities through the graph.

Learning binary representation is essential to large-scale computer vision tasks. Most existing algorithms require a separate quantization constraint to learn effective hashing functions. In this work, we present Direct Binary Embedding (DBE), a simple yet very effective algorithm to learn binary representation in an end-to-end fashion. By appending an ingeniously designed DBE layer to the deep convolutional neural network (DCNN), DBE learns binary code directly from the continuous DBE layer activation without quantization error.

Binary hashing is an established approach for fast, approximate image search. It maps a query image to a binary vector so that Hamming distances approximate image similarities. Applying the hash function can be made fast by using a circulant matrix and the fast Fourier transform, but this circulant hash function must be learned optimally from training data. We show that a previously proposed learning algorithm based on optimization in the frequency domain is suboptimal.