Nearest Neighbor Classification for Classical Image Upsampling
- URL: http://arxiv.org/abs/2403.19611v2
- Date: Thu, 15 Aug 2024 17:41:45 GMT
- Title: Nearest Neighbor Classification for Classical Image Upsampling
- Authors: Evan Matthews, Nicolas Prate,
- Abstract summary: We aim to perform upsampling on the data such that: the resulting resolution is improved by some factor, the final result passes the human test.
The time complexity for upscaling is relatively close to that of lossy upscaling implementations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Given a set of ordered pixel data in the form of an image, our goal is to perform upsampling on the data such that: the resulting resolution is improved by some factor, the final result passes the human test, having added new, believable, and realistic information and detail to the image, the time complexity for upscaling is relatively close to that of lossy upscaling implementations.
Related papers
- Towards Efficient and Accurate CT Segmentation via Edge-Preserving Probabilistic Downsampling [2.1465347972460367]
Downsampling images and labels, often necessitated by limited resources or to expedite network training, leads to the loss of small objects and thin boundaries.
This undermines the segmentation network's capacity to interpret images accurately and predict detailed labels, resulting in diminished performance compared to processing at original resolutions.
We introduce a novel method named Edge-preserving Probabilistic Downsampling (EPD)
It utilizes class uncertainty within a local window to produce soft labels, with the window size dictating the downsampling factor.
arXiv Detail & Related papers (2024-04-05T10:01:31Z) - Improving Feature Stability during Upsampling -- Spectral Artifacts and the Importance of Spatial Context [15.351461000403074]
Pixel-wise predictions are required in a wide variety of tasks such as image restoration, image segmentation, or disparity estimation.
Previous works have shown that resampling operations are subject to artifacts such as aliasing.
We show that the availability of large spatial context during upsampling allows to provide stable, high-quality pixel-wise predictions.
arXiv Detail & Related papers (2023-11-29T10:53:05Z) - Super-Resolution of License Plate Images Using Attention Modules and
Sub-Pixel Convolution Layers [3.8831062015253055]
We introduce a Single-Image Super-Resolution (SISR) approach to enhance the detection of structural and textural features in surveillance images.
Our approach incorporates sub-pixel convolution layers and a loss function that uses an Optical Character Recognition (OCR) model for feature extraction.
Our results show that our approach for reconstructing these low-resolution synthesized images outperforms existing ones in both quantitative and qualitative measures.
arXiv Detail & Related papers (2023-05-27T00:17:19Z) - Any-resolution Training for High-resolution Image Synthesis [55.19874755679901]
Generative models operate at fixed resolution, even though natural images come in a variety of sizes.
We argue that every pixel matters and create datasets with variable-size images, collected at their native resolutions.
We introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions.
arXiv Detail & Related papers (2022-04-14T17:59:31Z) - High-resolution Iterative Feedback Network for Camouflaged Object
Detection [128.893782016078]
Spotting camouflaged objects that are visually assimilated into the background is tricky for object detection algorithms.
We aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries.
We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner.
arXiv Detail & Related papers (2022-03-22T11:20:21Z) - Learning to Downsample for Segmentation of Ultra-High Resolution Images [6.432524678252553]
We show that learning the spatially varying downsampling strategy jointly with segmentation offers advantages in segmenting large images with limited computational budget.
Our method adapts the sampling density over different locations so that more samples are collected from the small important regions and less from the others.
We show on two public and one local high-resolution datasets that our method consistently learns sampling locations preserving more information and boosting segmentation accuracy over baseline methods.
arXiv Detail & Related papers (2021-09-22T23:04:59Z) - Toward Real-World Super-Resolution via Adaptive Downsampling Models [58.38683820192415]
This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge.
We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples.
arXiv Detail & Related papers (2021-09-08T06:00:32Z) - Adversarial Semantic Data Augmentation for Human Pose Estimation [96.75411357541438]
We propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity.
We also propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamiclly predict tailored pasting configuration.
State-of-the-art results are achieved on challenging benchmarks.
arXiv Detail & Related papers (2020-08-03T07:56:04Z) - High-Resolution Image Inpainting with Iterative Confidence Feedback and
Guided Upsampling [122.06593036862611]
Existing image inpainting methods often produce artifacts when dealing with large holes in real applications.
We propose an iterative inpainting method with a feedback mechanism.
Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
arXiv Detail & Related papers (2020-05-24T13:23:45Z) - Invertible Image Rescaling [118.2653765756915]
We develop an Invertible Rescaling Net (IRN) to produce visually-pleasing low-resolution images.
We capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process.
arXiv Detail & Related papers (2020-05-12T09:55:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.