Cutting-Edge Techniques for Depth Map Super-Resolution
To overcome hardware limitations in commercially available depth sensors which result in low-resolution depth maps, depth map super-resolution (DMSR) is a practical and valuable computer vision task. DMSR requires upscaling a low-resolution (LR) depth map into a high-resolution (HR) space. Joint image filtering for DMSR has been applied using spatially-invariant and spatially-variant convolutional neural network (CNN) approaches. In this project, we propose a novel joint image filtering DMSR algorithm using a Swin transformer architecture. Furthermore, we introduce a Nonlinear Activation Free (NAF) network based on a conventional CNN model used in cutting-edge image restoration applications and compare the performance of the techniques. The proposed algorithms are validated through numerical studies and visual examples demonstrating improvements to state-of-the-art performance while maintaining competitive computation time for noisy depth map super-resolution.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth Map Super-ResolutionImage RestorationSuper-ResolutionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition
Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable adv…
BenchmarkingMicro Expression RecognitionMicro-Expression RecognitionSuper-ResolutionSemantic-Aware Depth Super-Resolution in Outdoor Scenes
While depth sensors are becoming increasingly popular, their spatial resolution often remains limited. Depth super-resolution therefore emerged as a solution to this problem. Despite much progress, state-of-the-art techn…
Super-ResolutionLicense Plate Super-Resolution Using Diffusion Models
In surveillance, accurately recognizing license plates is hindered by their often low quality and small dimensions, compromising recognition precision. Despite advancements in AI-based image super-resolution, methods lik…
Image RestorationImage Super-ResolutionSSIMSuper-ResolutionA Review of Deep Learning Based Image Super-resolution Techniques
Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images. With the development of deep learning, image super-resolution technology based on deep learning…
Deep LearningImage Super-ResolutionSuper-ResolutionVariational Depth Superresolution Using Example-Based Edge Representations
In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth sup…