Related papers: Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing

Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing

URL: http://arxiv.org/abs/2306.09039v1
Date: Thu, 15 Jun 2023 10:59:29 GMT
Title: Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing
Authors: Zineddine Bettouche and Andreas Fischer
Abstract summary: This study looks into several processing methods that include high-pass filtering, autoencoding, and vectorization to extract an abstract representation of an image. According to the findings, rebuilding an image with autoencoders, high-pass filtering it, and then vectorizing it can represent the image more abstractly while increasing the effectiveness of the vectorization process.
Score: 0.6367279911825442
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: The process of transforming a raster image into a vector representation is known as image tracing. This study looks into several processing methods that include high-pass filtering, autoencoding, and vectorization to extract an abstract representation of an image. According to the findings, rebuilding an image with autoencoders, high-pass filtering it, and then vectorizing it can represent the image more abstractly while increasing the effectiveness of the vectorization process.

Related papers

SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis [66.44553285020066]
SuperSVG is a superpixel-based vectorization model that achieves fast and high-precision image vectorization. We propose a two-stage self-training framework, where a coarse-stage model is employed to reconstruct the main structure and a refinement-stage model is used for enriching the details. Experiments demonstrate the superior performance of our method in terms of reconstruction accuracy and inference time compared to state-of-the-art approaches.
arXiv Detail & Related papers (2024-06-14T07:43:23Z)
Layered Image Vectorization via Semantic Simplification [46.23779847614095]
This work presents a novel progressive image vectorization technique aimed at generating layered vectors that represent the original image from coarse to fine detail levels. Our approach introduces semantic simplification, which combines Score Distillation Sampling and semantic segmentation to iteratively simplify the input image. Our method provides robust optimization, which avoids local minima and enables adjustable detail levels in the final output.
arXiv Detail & Related papers (2024-06-08T08:54:35Z)
Unsupervised Deep Learning Image Verification Method [0.0]
The proposed method achieves a relative improvement of 56% in terms of EER over the baseline system on Labeled Faces in the Wild dataset. This has successfully narrowed down the performance gap between cosine and PLDA scoring systems.
arXiv Detail & Related papers (2023-12-22T02:52:54Z)
Optimize and Reduce: A Top-Down Approach for Image Vectorization [12.998637003026273]
We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing B'ezier curve parameters. We demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes.
arXiv Detail & Related papers (2023-12-18T16:41:03Z)
Perceptual Image Compression with Cooperative Cross-Modal Side Information [53.356714177243745]
We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
arXiv Detail & Related papers (2023-11-23T08:31:11Z)
VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models [82.93345261434943]
We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches.
arXiv Detail & Related papers (2022-11-21T10:04:27Z)
Lightweight Image Codec via Multi-Grid Multi-Block-Size Vector Quantization (MGBVQ) [37.36588620264085]
We present a new method to remove pixel correlations. By decomposing correlations into long- and short-range correlations, we represent long-range correlations in coarser grids. We show that short-range correlations can be effectively coded by a suite of vector quantizers.
arXiv Detail & Related papers (2022-09-25T04:14:26Z)
Study on Image Filtering -- Techniques, Algorithm and Applications [0.0]
Image filtering is a technique for altering the size, shape, color, depth, smoothness, and other image properties. This paper introduces various image filtering techniques and their wide applications.
arXiv Detail & Related papers (2022-06-04T15:54:21Z)
MaskGIT: Masked Generative Image Transformer [49.074967597485475]
MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. Experiments demonstrate that MaskGIT significantly outperforms the state-of-the-art transformer model on the ImageNet dataset.
arXiv Detail & Related papers (2022-02-08T23:54:06Z)
Swapping Autoencoder for Deep Image Manipulation [94.33114146172606]
We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation. The key idea is to encode an image with two independent components and enforce that any swapped combination maps to a realistic image. Experiments on multiple datasets show that our model produces better results and is substantially more efficient compared to recent generative models.
arXiv Detail & Related papers (2020-07-01T17:59:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.