Quarter Laplacian Filter for Edge Aware Image Processing
- URL: http://arxiv.org/abs/2101.07933v1
- Date: Wed, 20 Jan 2021 02:29:54 GMT
- Title: Quarter Laplacian Filter for Edge Aware Image Processing
- Authors: Yuanhao Gong, Wenming Tang, Lebin Zhou, Lantao Yu, Guoping Qiu
- Abstract summary: This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing.
We show its edge preserving property in several image processing tasks, including image smoothing, texture enhancement, and low-light image enhancement.
- Score: 32.885698849515045
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper presents a quarter Laplacian filter that can preserve corners and
edges during image smoothing. Its support region is $2\times2$, which is
smaller than the $3\times3$ support region of Laplacian filter. Thus, it is
more local. Moreover, this filter can be implemented via the classical box
filter, leading to high performance for real time applications. Finally, we
show its edge preserving property in several image processing tasks, including
image smoothing, texture enhancement, and low-light image enhancement. The
proposed filter can be adopted in a wide range of image processing
applications.
Related papers
- Filtering After Shading With Stochastic Texture Filtering [1.8377890861896995]
We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering before BSDF evaluation.
texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures.
arXiv Detail & Related papers (2024-05-14T16:42:07Z) - RSFNet: A White-Box Image Retouching Approach using Region-Specific
Color Filters [25.666027585116176]
We develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet.
Our model generates filter arguments and attention maps of regions for each filter simultaneously.
Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and increased user convenience for editable white-box retouching.
arXiv Detail & Related papers (2023-03-15T15:11:31Z) - Image Edge Restoring Filter [4.060948640328565]
We propose the image Edge Restoring Filter (ERF) to restore the blur edge pixels in the output of local smoothing filters to be clear.
The proposed filter can been implemented after many local smoothing filter.
arXiv Detail & Related papers (2021-12-27T07:02:01Z) - Reverse image filtering using total derivative approximation and
accelerated gradient descent [82.93345261434943]
We address a new problem of reversing the effect of an image filter, which can be linear or nonlinear.
The assumption is that the algorithm of the filter is unknown and the filter is available as a black box.
We formulate this inverse problem as minimizing a local patch-based cost function and use total derivative to approximate the gradient which is used in gradient descent to solve the problem.
arXiv Detail & Related papers (2021-12-08T05:16:11Z) - Learning Versatile Convolution Filters for Efficient Visual Recognition [125.34595948003745]
This paper introduces versatile filters to construct efficient convolutional neural networks.
We conduct theoretical analysis on network complexity and an efficient convolution scheme is introduced.
Experimental results on benchmark datasets and neural networks demonstrate that our versatile filters are able to achieve comparable accuracy as that of original filters.
arXiv Detail & Related papers (2021-09-20T06:07:14Z) - One-Sided Box Filter for Edge Preserving Image Smoothing [9.67565473617028]
We present a one-sided box filter that can smooth the signal but keep the discontinuous features in the signal.
More specifically, we perform box filter on eight one-sided windows, leading to a one-sided box filter that can preserve corners and edges.
arXiv Detail & Related papers (2021-08-11T04:22:38Z) - Unsharp Mask Guided Filtering [53.14430987860308]
The goal of this paper is guided image filtering, which emphasizes the importance of structure transfer during filtering.
We propose a new and simplified formulation of the guided filter inspired by unsharp masking.
Our formulation enjoys a filtering prior to a low-pass filter and enables explicit structure transfer by estimating a single coefficient.
arXiv Detail & Related papers (2021-06-02T19:15:34Z) - Adaptive Debanding Filter [55.42929350861115]
Banding artifacts manifest as staircase-like color bands on pictures or video frames.
We propose a content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module.
Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.
arXiv Detail & Related papers (2020-09-22T20:44:20Z) - Recognizing Instagram Filtered Images with Feature De-stylization [81.38905784617089]
This paper presents a study on how popular pretrained models are affected by commonly used Instagram filters.
Our analysis suggests that simple structure preserving filters which only alter the global appearance of an image can lead to large differences in the convolutional feature space.
We introduce a lightweight de-stylization module that predicts parameters used for scaling and shifting feature maps to "undo" the changes incurred by filters.
arXiv Detail & Related papers (2019-12-30T16:48:16Z)
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.