Papers Image Defocus Deblurring
“Image Defocus Deblurring” 태그가 달린 논문 35편 · 필터 해제
EENet: Frequency-Aware and Spatially Multiscale Network for Single Image Dehazing
While numerous solutions leveraging convolutional neural networks and Transformers have been proposed for image dehazing, there remains significant potential to improve the balance between efficiency and reconstruction p…
DeblurringImage Defocus DeblurringImage DehazingImage Enhancement+2Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks
Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distr…
DeblurringImage Defocus DeblurringTest-time AdaptationQuad-Pixel Image Defocus Deblurring: A New Benchmark and Model
Defocus deblurring is a challenging task due to the spatially varying blur. Recent works have shown impressive results in data-driven approaches using dual-pixel (DP) sensors. Quad-pixel (QP) sensors represent an adv…
DeblurringImage Defocus DeblurringMambaReblurring-Guided Single Image Defocus Deblurring: A Learning Framework with Misaligned Training Pairs
For single image defocus deblurring, acquiring well-aligned training pairs (or training triplets), i.e., a defocus blurry image, an all-in-focus sharp image (and a defocus blur map), is an intricate task for the developm…
DeblurringImage Defocus DeblurringLearning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
Image deblurring aims to restore a high-quality image from its corresponding blurred. The emergence of CNNs and Transformers has enabled significant progress. However, these methods often face the dilemma between elimina…
Computational EfficiencyDeblurringImage DeblurringImage Defocus DeblurringOmni-Kernel Network for Image Restoration
Image restoration aims to reconstruct a high-quality image from a degraded low-quality observation. Recently, Transformer models have achieved promising performance on image restoration tasks due to their powerful abilit…
DeblurringImage Defocus DeblurringImage DehazingImage Restoration+1Dual-domain strip attention for image restoration
Image restoration aims to reconstruct a latent high-quality image from a degraded observation. Recently, the usage of Transformer has significantly advanced the state-of-the-art performance of various image restoration t…
DeblurringDenoisingImage Defocus DeblurringImage Dehazing+3Exploring the potential of channel interactions for image restoration
Image restoration aims to reconstruct a clear image from a degraded observation. Convolutional neural networks have achieved promising performance on this task. The usage of Transformer has recently made significant adva…
DeblurringImage DeblurringImage Defocus DeblurringImage Dehazing+5Image Restoration via Frequency Selection
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain by …
DeblurringDenoisingImage DeblurringImage Defocus Deblurring+4SR-R$^2$KAC: Improving Single Image Defocus Deblurring
We propose an efficient deep learning method for single image defocus deblurring (SIDD) by further exploring inverse kernel properties. Although the current inverse kernel method, i.e., kernel-sharing parallel atrous con…
DeblurringImage Defocus DeblurringLDP: Language-driven Dual-Pixel Image Defocus Deblurring Network
Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task.~Existing blur map-based deblurring methods have demonstrated promising results. In this paper, we propose, to the be…
DeblurringImage Defocus DeblurringSelective Frequency Network for Image Restoration
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart. Besides dealing with this long-standing task in the spatial domain, a few approaches seek solutions in the frequency domain in …
DeblurringImage Defocus DeblurringImage DehazingImage Restoration+2Masked Autoencoders as Image Processors
Transformers have shown significant effectiveness for various vision tasks including both high-level vision and low-level vision. Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the po…
DeblurringDenoisingImage Defocus DeblurringImage Denoising+1Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important p…
Image DeblurringImage Defocus DeblurringImage RestorationImage Super-ResolutionRevisiting Image Deblurring with an Efficient ConvNet
Image deblurring aims to recover the latent sharp image from its blurry counterpart and has a wide range of applications in computer vision. The Convolution Neural Networks (CNNs) have performed well in this domain for m…
AttributeDeblurringImage DeblurringImage Defocus DeblurringNeumann Network With Recursive Kernels for Single Image Defocus Deblurring
Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant …
DeblurringImage Defocus DeblurringFocal Network for Image Restoration
Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields. Recently, Transformer models have achieved promising performance on various image resto…
DeblurringImage Defocus DeblurringImage DehazingImage RestorationSingle Image Defocus Deblurring via Implicit Neural Inverse Kernels
Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs). Existing deep-learning-based methods for SID…
DeblurringImage Defocus DeblurringLearning Single Image Defocus Deblurring with Misaligned Training Pairs
By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on well aligned training image pairs. Although training pairs of ground-truth and blurry images are carefully collected, e.g., DPD…
DeblurringImage Defocus DeblurringOptical Flow EstimationLearnable Blur Kernel for Single-Image Defocus Deblurring in the Wild
Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image defocus deblurring. However, extracting real-time dual-pixel views is troublesome and complex in algorithm dep…
DeblurringGenerative Adversarial NetworkImage DeblurringImage Defocus Deblurring