Revisiting Implicit Neural Representations in Low-Level Vision
Implicit Neural Representation (INR) has been emerging in computer vision in recent years. It has been shown to be effective in parameterising continuous signals such as dense 3D models from discrete image data, e.g. the neural radius field (NeRF). However, INR is under-explored in 2D image processing tasks. Considering the basic definition and the structure of INR, we are interested in its effectiveness in low-level vision problems such as image restoration. In this work, we revisit INR and investigate its application in low-level image restoration tasks including image denoising, super-resolution, inpainting, and deblurring. Extensive experimental evaluations suggest the superior performance of INR in several low-level vision tasks with limited resources, outperforming its counterparts by over 2dB. Code and models are available at https://github.com/WenTXuL/LINR
Code (1)
Tasks
DeblurringDenoisingImage DenoisingImage RestorationNeRFSuper-ResolutionSimilar Papers 제목 키워드 기반
Rethinking Implicit Neural Representations for Vision Learners
Implicit Neural Representations (INRs) are powerful to parameterize continuous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., image/video compression, super-resolution,…
image-classificationImage ClassificationImage GenerationInstance Segmentation+5BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds
We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several…
Semantic SegmentationTwins: Revisiting the Design of Spatial Attention in Vision Transformers
Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we …
Image ClassificationSemantic SegmentationRefining Implicit Argument Annotation for UCCA
Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, …
Coreference ResolutionNatural Language UnderstandingQuestion AnsweringSentenceMAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning
Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision and language while lacking effective fin…
Image-text Retrievalmultimodal interactionQuestion AnsweringRepresentation Learning+6