Image-guided ToF depth upsampling: a survey

Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies

Fusing spatial and temporal components for real-time depth data enhancement of dynamic scenes

The depth images from consumer depth cameras (e.g., structured-light/ToF devices) exhibit a substantial amount of artifacts (e.g., holes, flickering, ghosting) that needs to be removed for real-world applications. Existing methods cannot entirely remove them and perform slow. This thesis proposes a new real-time spatio-temporal depth image enhancement filter that completely removes flickering and ghosting, and significantly reduces holes. This thesis also presents a novel depth-data capture setup and two data reduction methods to optimize the performance of the proposed enhancement method

Learning to Enhance RGB and Depth Images with Guidance

Image enhancement improves the visual quality of the input image to better identify key features and make it more suitable for other vision applications. Structure degradation remains a challenging problem in image enhancement, which refers to blurry edges or discontinuous structures due to unbalanced or inconsistent intensity transitions on structural regions. To overcome this issue, it is popular to make use of a guidance image to provide additional structural cues. In this thesis, we focus on two image enhancement tasks, i.e., RGB image smoothing and depth image completion. Through the two research problems, we aim to have a better understanding of what constitutes suitable guidance and how its proper use can benefit the reduction of structure degradation in image enhancement. Image smoothing retains salient structures and removes insignificant textures in an image. Structure degradation results from the difficulty in distinguishing structures and textures with low-level cues. Structures may be inevitably blurred if the filter tries to remove some strong textures that have high contrast. Moreover, these strong textures may also be mistakenly retained as structures. We address this issue by applying two forms of guidance for structures and textures respectively. We first design a kernel-based double-guided filter (DGF), where we adopt semantic edge detection as structure guidance, and texture decomposition as texture guidance. The DGF is the first kernel filter that simultaneously leverages structure guidance and texture guidance to be both ''structure-aware'' and ''texture-aware''. Considering that textures present high randomness and variations in spatial distribution and intensities, it is not robust to localize and identify textures with hand-crafted features. Hence, we take advantage of deep learning for richer feature extraction and better generalization. Specifically, we generate synthetic data by blending natural textures with clean structure-only images. With the data, we build a texture prediction network (TPN) that estimates the location and magnitude of textures. We then combine the texture prediction results from TPN with a semantic structure prediction network so that the final texture and structure aware filtering network (TSAFN) is able to distinguish structures and textures more effectively. Our model achieves superior smoothing results than existing filters. Depth completion recovers dense depth from sparse measurements, e.g., LiDAR. Existing depth-only methods use sparse depth as the only input and suffer from structure degradation, i.e., failing to recover semantically consistent boundaries or small/thin objects due to (1) the sparse nature of depth points and (2) the lack of images to provide structural cues. In the thesis, we deal with the structure degradation issue by using RGB image guidance in both supervised and unsupervised depth-only settings. For the supervised model, the unique design is that it simultaneously outputs a reconstructed image and a dense depth map. Specifically, we treat image reconstruction from sparse depth as an auxiliary task during training that is supervised by the image. For the unsupervised model, we regard dense depth as a reconstructed result of the sparse input, and formulate our model as an auto-encoder. To reduce structure degradation, we employ the image to guide latent features by penalizing their difference in the training process. The image guidance loss in both models enables them to acquire more dense and structural cues that are beneficial for producing more accurate and consistent depth values. For inference, the two models only take sparse depth as input and no image is required. On the KITTI Depth Completion Benchmark, we validate the effectiveness of the proposed image guidance through extensive experiments and achieve competitive performance over state-of-the-art supervised and unsupervised methods. Our approach is also applicable to indoor scenes

Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information