| 1. |
- Xia, Z., et al.
(författare)
-
Geometrical attacks resilient statistical watermark decoder using polar harmonic Fourier moments
- 2023
-
Ingår i: Journal of the Franklin Institute. - : Elsevier BV. - 0016-0032 .- 1879-2693. ; 360:7, s. 4493-4518
-
Tidskriftsartikel (refereegranskat)abstract
- This paper presents a new robust multiplicative watermark detector. Due to the strong robustness against various attacks, polar harmonic Fourier moment (PHFM) magnitudes are used as the employed watermark carrier. The distribution of PHFM magnitudes is highly non-Gaussian and can be properly modeled by a heavy-tailed probability density function (PDF). In this paper, we proved that Weibull distribution can suitably fit the distribution of PHFM magnitudes, and based on this, we presented a statistics-based watermark decoder by using the Weibull as a prior for the PHFM magnitudes. In watermark embedding, a multiplicative manner was used to embed watermark information in PHFM magnitudes of the highest entropy blocks to achieve better robustness and imperceptibility. In watermark detection, we developed a Weibull distribution-based statistical watermark decoder, which uses the maximum likelihood (ML) decision rule. Compared with Bessel K form (BKF), Cauchy, and generalized Gaussian (GG)-based decoders, the Weibull-based decoder demonstrates stronger robustness. In addition, the proposed watermark decoder is more robust against geometrical and common image processing attacks than existing statistical watermark decoders.
|
|
| 2. |
|
|
| 3. |
- Li, Yongwei, 1990-
(författare)
-
Computational Light Field Photography : Depth Estimation, Demosaicing, and Super-Resolution
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The transition of camera technology from film-based cameras to digital cameras has been witnessed in the past twenty years, along with impressive technological advances in processing massively digitized media content. Today, a new evolution emerged -- the migration from 2D content to immersive perception. This rising trend has a profound and long-term impact to our society, fostering technologies such as teleconferencing and remote surgery. The trend is also reflected in the scientific research community, and more intention has been drawn to the light field and its applications. The purpose of this dissertation is to develop a better understanding of light field structure by analyzing its sampling behavior and to addresses three problems concerning the light field processing pipeline: 1) How to address the depth estimation problem when there is limited color and texture information. 2) How to improve the rendered image quality by using the inherent depth information. 3) How to solve the interdependence conflict of demosaicing and depth estimation. The first problem is solved by a hybrid depth estimation approach that combines advantages of correspondence matching and depth-from-focus, where occlusion is handled by involving multiple depth maps in a voting scheme. The second problem is divided into two specific tasks -- demosaicing and super-resolution, where depth-assisted light field analysis is employed to surpass the competence of traditional image processing. The third problem is tackled with an inferential graph model that encodes the connections between demosaicing and depth estimation explicitly, and jointly performs a global optimization for both tasks. The proposed depth estimation approach shows a noticeable improvement in point clouds and depth maps, compared with references methods. Furthermore, the objective metrics and visual quality are compared with classical sensor-based demosaicing and multi-image super-resolution to show the effectiveness of the proposed depth-assisted light field processing methods. Finally, a multi-task graph model is proposed to challenge the performance of the sequential light field image processing pipeline. The proposed method is validated with various kinds of light fields, and outperforms the state-of-the-art in both demosaicing and depth estimation tasks. The works presented in this dissertation raise a novel view of the light field data structure in general, and provide tools to solve image processing problems in specific. The impact of the outcome can be manifold: To support scientific research with light field microscopes, to stabilize the performance of range cameras for industrial applications, as well as to provide individuals with a high-quality immersive experience.
|
|
| 4. |
- Li, Yongwei, 1990-, et al.
(författare)
-
Depth-Assisted Light Field Super-Resolution in Layered Object Space
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The captured light field may fail to reconstruct fine details of the scene due to under-sampling problem of lightfield acquisition devices. Therefore,super-resolution is required to restore high-frequency information from the light field and to improve the quality of therendered views. Conventionalsuper-resolution algorithms are not ideal for light field data, as they do not utilize the full potential of light field 4D structure, while existing light fieldsuper-resolution algorithms rely heavily on the accuracy of the estimated depth and perform complex sub-pixeldisparity estimation. In this paper, we propose a new light field super-resolution algorithm which can address depthuncertainty with a layered object space. First, a pixel-wise depth estimation is performed from the resampled views.Then we divide the depth range into finite layers and back-project pixels onto these layers in order to address the sub-pixel depth error. Finally, two super-resolution schemes: in-depth warping and cross-depth learning, are introduced tosuper-resolve the views from light field data redundancy. The algorithms is tested with extensive datasets, and theresults show that our method attains favorable results in both visual assessment and objective metrics compared toother light field super-resolution methods.
|
|
| 5. |
- Li, Yongwei, 1990-, et al.
(författare)
-
Simultaneous Color Restoration and Depth Estimation in Light Field Imaging
- 2022
-
Ingår i: IEEE Access. - 2169-3536. ; 10, s. 49599-49610
-
Tidskriftsartikel (refereegranskat)abstract
- Recent studies in the light field imaging have shown the potential and advantages of different light field information processes. In most of the existing techniques, the processing pipeline of light field has been treated in a step-by-step manner, and each step is considered to be independent from the others. For example, in light field color demosaicing, inferring the scene geometry is treated as an irrelevant and negligible task, and vice versa. Such processing techniques may fail due to the inherent connection among different steps, and result in both corrupted post-processing and defective pre-processing results. In this paper, we address the interaction between color interpolation and depth estimation in light field, and propose a probabilistic approach to handle these two processing steps jointly. This probabilistic framework is based on a Markov Random Fields —Collaborative Graph Model for simultaneous Demosaicing and Depth Estimation (CGMDD)—to explore the color-depth interdependence from general light field sampling. Experimental results show that both image interpolation quality and depth estimation can benefit from their interaction, mainly for processes such as image demosaicing which are shown to be sensitive to depth information, especially for light field sampling with large baselines.
|
|
