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

Unpaired image-to-image translation is a tricky task which aims at learning a mapping from one image collection to another image collection without any pair-labeled information. Recent works have proposed cycle-consistency assumption to deal with this task. However, the result is still unsatisfactory for geometric translation. To address this limitation, this paper proposes a novel method using shared deep space generative adversarial network (SDSGAN).

The Discrete Periodic Radon Transform (DPRT) has many important applications in reconstructing images from their projections and has recently been used in fast and scalable architectures for computing 2D convolutions. Unfortunately, the direct computation of the DPRT involves O(N^3) additions and memory accesses that can be very costly in single-core architectures.

In this paper, we propose the first joint watermarking-encryption-compression scheme for the protection of medical images. Its originality is twofold. In a first time, it allows the access to watermarking-based security services from the encrypted and the compressed bitstreams without having to parse them even partially. It becomes possible to trace images and control their reliability (i.e. integrity and authenticity) from both the encrypted and compressed domains.

This paper presents a method for stabilizing both cylindrical and spherical panorama videos with a 360-degree field of view. We observe that rotation needs to be extremely smooth for 360 videos to maintain global motion coherency and avoid wobbling. Our method decouples the rotation from other motions and applies different strategies for smoothing them. The proposed approach is 2.5D as it estimates 3D rotations without involving 3D structure-from-motion methods. Therefore, it is more robust and can be performed in an incremental way.

Fourier Transform Interferometry (FTI) is an interferometric procedure for acquiring HyperSpectral (HS) data. Recently, it has been observed that the light source highlighting a (biologic) sample can be coded before the FTI acquisition in a procedure called Coded Illumination-FTI (CI-FTI). This turns HS data reconstruction into a Compressive Sensing (CS) problem regularized by the sparsity of the HS data. CI-FTI combines the high spectral resolution of FTI with the advantages of reduced-light-exposure imaging in biology.

This paper proposes a swung-to-cylinder projection model for mapping a sphere to a plane. It can be used to create a semi-perspective image from a panoramic image. The model has two steps. In the first step, the sphere is projected onto a swung surface constructed by a circular profile and a rounded rectangular trajectory. In the second step, the projected image on the swung surface is mapped onto a cylinder through the perspective projection. We also propose methods for automatically determining proper parameters for the projection model based on image content.

The manuscript describes fast and scalable architectures and associated algorithms for computing convolutions and cross-correlations. The basic idea is to map 2D convolutions and cross-correlations to a collection of 1D convolutions and cross-correlations in the transform domain. This is accomplished through the use of the Discrete Periodic Radon Transform (DPRT) for general kernels and the use of SVD-LU decompositions for low-rank kernels.