A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Investigating submerged morphologies by means of the low-budget “GeoDive” method (high resolution for detailed 3D reconstruction and related measurements)

Geophysical methods allow to collect geological data on lake and sea bottoms and characterize large areas, even at high depths, but with high costs. Moreover, the most widespread acquisition methods for morpho-bathymetric survey and the related instruments used are almost always ship-, ROV- or AUV-based and consequently they require high budgets. It is known that shallow waters can represent a limit for certain vessels and techniques, preventing the acquisition in the shoreface zone. To overcome the limits, i.e. to survey with high accuracy nearshore shallow waters with a low budget, we tested and tuned the “GeoDive” method that allowed us to survey two test sites, featured by the presence of “block fields” (i.e., accumulations of huge blocks and boulders of gravitational origin) under shallow waters. The “GeoDive” method allowed us to map the submerged morphologies and to acquire high-resolution optical images for further photogrammetric processing. The latter was fundamental to obtain 3D high-resolution models, also with conditions of low visibility. An Action Sport Cam with high definition resolution has been used for video acquisition, in addition to the equipment used during scientific diving. By coupling the processing of underwater-acquired data with the direct surveys performed by underwater SCUBA operators, it was possible to perform some morphological and sedimentological measurements and observations on the experimental targets, with the help of suitable markers

Euclidean reconstruction of natural underwater scenes using optic imagery sequence

The development of maritime applications require monitoring, studying and preserving of detailed and close observation on the underwater seafloor and objects. Stereo vision offers advanced technologies to build 3D models from 2D still overlapping images in a relatively inexpensive way. However, while image stereo matching is a necessary step in 3D reconstruction procedure, even the most robust dense matching techniques are not guaranteed to work for underwater images due to the challenging aquatic environment. In this thesis, in addition to a detailed introduction and research on the key components of building 3D models from optic images, a robust modified quasi-dense matching algorithm based on correspondence propagation and adaptive least square matching for underwater images is proposed and applied to some typical underwater image datasets. The experiments demonstrate the robustness and good performance of the proposed matching approach