18 skills found
codeslake / DMENet[CVPR 2019] Official TensorFlow Implementation for "Deep Defocus Map Estimation using Domain Adaptation"
shirgur / UnsupervisedDepthFromFocusSingle Image Depth Estimation Trained via Depth from Defocus Cues
vinthony / Depth Distillation[ECCV 2020] Defocus Blur Detection via Depth Distillation
computational-imaging / DepthFromDefocusWithLearnedOpticsICCP2021: Depth from Defocus with Learned Optics for Imaging and Occlusion-aware Depth Estimation
xinario / Defocus SegmentationLBP-based segmentation of defocus blur
CodeMonsterPHD / GaTector A Unified Framework For Gaze Object PredictionThis repository is the official implementation of GaTector, which studies the newly proposed task, gaze object prediction. In this work, we build a novel framework named GaTector to tackle the gaze object prediction problem in a unified way. Particularly, a specific-general-specific (SGS) feature extractor is firstly proposed to utilize a shared backbone to extract general features for both scene and head images. To better consider the specificity of inputs and tasks, SGS introduces two input-specific blocks before the shared backbone and three task-specific blocks after the shared backbone. Specifically, a novel defocus layer is designed to generate object-specific features for object detection task without losing information or requiring extra computations. Moreover, the energy aggregation loss is introduced to guide the gaze heatmap to concentrate on the stared box. In the end, we propose a novel mDAP metric that can reveal the difference between boxes even when they share no overlapping area. Extensive experiments on the GOO dataset verify the superiority of our method in all three tracks, i.e., object detection, gaze estimation, and gaze object prediction.
zzangjinsun / DHDE CVPR17A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation
codeslake / SYNDOFThe official matlab implementation of SYNDOF generation used in the paper, 'Deep Defocus Map Estimation using Domain Adaptation', CVPR 2019
Ehzoahis / DEReDThe Offical Codebase for Fully Self-Supervised Depth Estimation from Defocus Clue
cmu-ci-lab / Dual Pixel Defocus Estimation DeblurringNo description available
xytmhy / DID ANet Defocus DeblurringCode for our paper DED-Net: the Dual-task Network for Defocus Estimation and Deblurring (Under Construction Now)
vllab / Fast Defocus MapFast Defocus Map Estimation
alikaraali / Edge Based Dfe Tip2018A. Karaali, CR. Jung, "Edge-Based Defocus Blur Estimation with Adaptive Scale Selection", IEEE Transactions on Image Processing (TIP 2018), 2018
alikaraali / Depth Edge Aware Dfe Tip2022Code of the paper https://arxiv.org/abs/2009.11939. A defocus blur estimation method.
alikaraali / Dfe Pr2010Implementation of "Defocus map estimation from a single image", S. Zhuo, T. Sim - Pattern Recognition, 2011 - Elsevier
weifei7 / Defocus Blur Detection And Defocus Map Estimation PapersA collection of deep learning based defocus blur detection and defocus map estimation papers.
nietowl / BLUR DETECTION BotMany digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image deblurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval.
ronaldpan / Multi View Images Defocus Detectionsample data for Defocus blur estimation in calibrated multi-view images
