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Generating Natural Images with Direct Patch Distributions Matching
Many traditional computer vision algorithms generate realistic images by requiring that each patch in the generated image be similar to a patch in a...
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Real-time and on-line removal of moving human figures in hand-held mobile augmented reality
In this paper, we present a real time on-line augmented/diminished reality system that runs entirely on the hand-held moving mobile device....
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Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington’s Disease
Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn...
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MFNet: Multi-level fusion aware feature pyramid based multi-view stereo network for 3D reconstruction
We present an efficient multi-view stereo (MVS) network for 3D reconstruction from multi-view images. While the existing state-of-the-art methods...
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MVSTER: Epipolar Transformer for Efficient Multi-view Stereo
Learning-based Multi-View Stereo (MVS) methods warp source images into the reference camera frustum to form 3D volumes, which are fused as a cost...
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PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We...
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Non-local Haze Propagation with an Iso-Depth Prior
The primary challenge for removing haze from a single image is lack of decomposition cues between the original light transport and airlight...
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Face attribute analysis from structured light: an end-to-end approach
In this work we explore the use of structured-light imaging for face analysis. Towards this and due to lack of a publicly available structured-light...
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Image resizing by reconstruction from deep features
Traditional image resizing methods usually work in pixel space and use various saliency measures. The challenge is to adjust the image shape while...
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DKH: a hybridized approach for image inpainting using Bayes probabilistic-based image fusion
Image inpainting is the process of removing the unwanted objects from the image or it refers to the restoration of the original image. Despite the...
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A survey on digital image forensic methods based on blind forgery detection
In the current digital era, images have become one of the key channels for communication and information. There are multiple platforms where digital...
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Pyramid-VAE-GAN: Transferring hierarchical latent variables for image inpainting
Significant progress has been made in image inpainting methods in recent years. However, they are incapable of producing inpainting results with...
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Copy-move tampering detection using keypoint based hybrid feature extraction and improved transformation model
Digitally tampered images or fake images when propagated across the Web and social media, have the power to mislead, emotionally distress and...
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Deep Image Compression Using Decoder Side Information
We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on...
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Guiding image inpainting via structure and texture features with dual encoder
Image inpainting techniques have made rapid progresses in recent years. Recent advancements focus mainly on generating realistic and semantically...
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The improved image inpainting algorithm via encoder and similarity constraint
Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible...
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A Benchmark for Inpainting of Clothing Images with Irregular Holes
Fashion image understanding is an active research field with a large number of practical applications for the industry. Despite its practical impacts...
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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features
Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency...
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MEStereo-Du2CNN: a dual-channel CNN for learning robust depth estimates from multi-exposure stereo images for HDR 3D applications
Display technologies have evolved over the years. It is critical to develop practical HDR capturing, processing, and display solutions to bring 3D...
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Self-supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images
Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth...