IEEE TCI Article

We present an all-in-one camera model that encompasses the architectures of most existing compressive-sensing light-field cameras, equipped with a single lens and multiple amplitude coded masks that can be placed at different positions between the lens and the sensor. The proposed model, named the equivalent multi-mask camera (EMMC) model, enables the comparison between different camera designs, e.g using monochrome or CFA-based sensors, single or multiple acquisitions, or varying pixel sizes, via a simple adaptation of the sampling operator. 

Recently, deep learning-based compressive imaging (DCI) has surpassed conventional compressive imaging in reconstruction quality and running speed. While multi-scale sampling has shown superior performance over single-scale, research in DCI has been limited to single-scale sampling. Despite training with single-scale images, DCI tends to favor low-frequency components similar to conventional multi-scale sampling, especially at low subrates. 

In this article, we propose a method to reconstruct the total electromagnetic field in an arbitrary two-dimensional scattering environment without any prior knowledge of the incident field or the permittivities of the scatterers. However, we assume that the region between the scatterers is homogeneous and that the approximate geometry describing the environment is known.

Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or ultrasound, to directly measure focus state. However, such sensors increase cost and complexity without directly optimizing final image quality. This paper proposes a new pipeline for image-based autofocus and shows that neural image analysis finds focus 5-10x faster than traditional contrast enhancement. 

Non-line-of-sight (NLOS) imaging and tracking is an emerging technology that allows the shape or position of objects around corners or behind diffusers to be recovered from transient, time-of-flight measurements. However, existing NLOS approaches require the imaging system to scan a large area on a visible surface, where the indirect light paths of hidden objects are sampled.

Three-dimensional reconstruction of tomograms from optical projection microscopy is confronted with several drawbacks. In this paper we employ iterative reconstruction algorithms to avoid streak artefacts in the reconstruction and explore possible ways to optimize two parameters of the algorithms, i.e., iteration number and initialization, in order to improve the reconstruction performance. As benchmarks for direct reconstruction evaluation in optical projection tomography are absent, we consider the assessment through the performance of the segmentation on the 3D reconstruction. In our explorative experiments we use the zebrafish model system which is a typical specimen for use in optical projection tomography system; and as such frequently used.

Recently, deep-learning based methods have been widely used for computed tomography (CT) reconstruction. However, most of these methods need extra steps to convert the sinogrmas into CT images and so their networks are not end-to-end. In this paper, we propose an end-to-end deep network for CT image reconstruction, which directly maps sparse sinogramss to CT images. Our network has three cascaded blocks, where the first block is used to denoise and interpolate the sinograms, the second to map the sinograms to CT images and the last to denoise the CT images.

The use of microwave tomography (MWT) in an industrial drying process is demonstrated in this feasibility study with synthetic measurement data. The studied imaging modality is applied to estimate the moisture content distribution in a polymer foam during the microwave drying process. Such moisture information is crucial in developing control strategies for controlling the microwave power for selective heating.

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with lowdose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise, and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object.

The modeling of phenomenological structure is a crucial aspect in inverse imaging problems. One emerging modeling tool in computer vision is the optimal transport framework. Its ability to model geometric displacements across an image's support gives it attractive qualities similar to optical flow methods that are effective at capturing visual motion, but are restricted to operate in significantly smaller state-spaces.