Papers Defocus Blur Detection
“Defocus Blur Detection” 태그가 달린 논문 15편 · 필터 해제
FOCUS: Towards Universal Foreground Segmentation
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unificati…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationSalient Object Detection+2Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
Histopathology is a gold standard for cancer diagnosis under a microscopic examination. However, histological tissue processing procedures result in artifacts, which are ultimately transferred to the digitized version of…
Airbubbles DetectionAnomaly DetectionArtifact DetectionBlood Detection+7Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. T…
Anomaly DetectionArtifact DetectionDefocus Blur DetectionDiagnostic+4Depth and DOF Cues Make A Better Defocus Blur Detector
Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions, likely due to not considering the internal…
Defocus Blur DetectionDepth EstimationMonocular Depth EstimationExplicit Visual Prompting for Universal Foreground Segmentations
Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works h…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationImage Manipulation Detection+4Explicit Visual Prompting for Low-Level Structure Segmentations
We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow regions, and detecting concealed objects. W…
Camouflaged Object SegmentationDefocus Blur DetectionForeground SegmentationImage Manipulation Detection+3Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection
Defocus blur detection (DBD) aims to segment the blurred regions from a given image affected by defocus blur. It is a crucial pre-processing step for various computer vision tasks. With the increasing popularity of small…
Defocus Blur DetectionGenerative Adversarial NetworkKnowledge DistillationSelf-Generated Defocus Blur Detection via Dual Adversarial Discriminators
Although existing fully-supervised defocus blur detection (DBD) models significantly improve performance, training such deep models requires abundant pixel-level manual annotation, which is highly time-consuming and …
Defocus Blur DetectionTripletDefocus Blur Detection via Salient Region Detection Prior
Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-…
Defocus Blur DetectionRethinking the Defocus Blur Detection Problem and A Real-Time Deep DBD Model
Defocus blur detection (DBD) is a classical low level vision task. It has recently attracted attention focusing on designing complex convolutional neural networks (CNN) which make full use of both low level features and …
Data AugmentationDefocus Blur DetectionDefocus Blur Detection via Depth Distillation
Defocus Blur Detection(DBD) aims to separate in-focus and out-of-focus regions from a single image pixel-wisely. This task has been paid much attention since bokeh effects are widely used in digital cameras and smartphon…
DecoderDefocus Blur DetectionDepth EstimationGPU+1DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features
Defocus blur detection aims to detect out-of-focus regions from an image. Although attracting more and more attention due to its widespread applications, defocus blur detection still confronts several challenges such as …
Defocus Blur DetectionDefocus EstimationSensitivityEnhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network
Defocus blur detection (DBD) is a fundamental yet challenging topic, since the homogeneous region is obscure and the transition from the focused area to the unfocused region is gradual. Recent DBD methods make progress t…
Defocus Blur DetectionDefocus EstimationDiversityDefocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network
Defocus blur detection (DBD) is the separation of infocus and out-of-focus regions in an image. This process has been paid considerable attention because of its remarkable potential applications. Accurate differentiation…
Defocus Blur DetectionDefocus EstimationJust Noticeable Defocus Blur Detection and Estimation
We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not stron…
Defocus Blur Detection