610 research outputs found
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
Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution
Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images
Learning Enriched Features for Real Image Restoration and Enhancement
With the goal of recovering high-quality image content from its degraded
version, image restoration enjoys numerous applications, such as in
surveillance, computational photography, medical imaging, and remote sensing.
Recently, convolutional neural networks (CNNs) have achieved dramatic
improvements over conventional approaches for image restoration task. Existing
CNN-based methods typically operate either on full-resolution or on
progressively low-resolution representations. In the former case, spatially
precise but contextually less robust results are achieved, while in the latter
case, semantically reliable but spatially less accurate outputs are generated.
In this paper, we present a novel architecture with the collective goals of
maintaining spatially-precise high-resolution representations through the
entire network and receiving strong contextual information from the
low-resolution representations. The core of our approach is a multi-scale
residual block containing several key elements: (a) parallel multi-resolution
convolution streams for extracting multi-scale features, (b) information
exchange across the multi-resolution streams, (c) spatial and channel attention
mechanisms for capturing contextual information, and (d) attention based
multi-scale feature aggregation. In a nutshell, our approach learns an enriched
set of features that combines contextual information from multiple scales,
while simultaneously preserving the high-resolution spatial details. Extensive
experiments on five real image benchmark datasets demonstrate that our method,
named as MIRNet, achieves state-of-the-art results for a variety of image
processing tasks, including image denoising, super-resolution, and image
enhancement. The source code and pre-trained models are available at
https://github.com/swz30/MIRNet.Comment: Accepted for publication at ECCV 202
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