Image Edge Restoring Filter
- URL: http://arxiv.org/abs/2112.13540v1
- Date: Mon, 27 Dec 2021 07:02:01 GMT
- Title: Image Edge Restoring Filter
- Authors: Qian Liu, Yongpeng Li, Zhihang Wang
- Abstract summary: 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.
- Score: 4.060948640328565
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In computer vision, image processing and computer graphics, image smoothing
filtering is a very basic and important task and to be expected possessing good
edge-preserving smoothing property. Here we address the problem that the
edge-preserving ability of many popular local smoothing filters needs to be
improved. In this paper, 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 (such as Box filter, Gaussian filter, Bilateral Filter, Guided Filter
and so on). The combinations of "original local smoothing filters + ERF" have
better edge-preserving smoothing property than the original local smoothing
filters. Experiments on image smoothing, image denoising and image enhancement
demonstrate the excellent edges restoring ability of the proposed filter and
good edgepreserving smoothing property of the combination "original local
smoothing filters + ERF". The proposed filter would benefit a great variety of
applications given that smoothing filtering is a high frequently used and
fundamental operation.
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 Restoration in Non-Linear Filtering Domain using MDB approach [0.0]
The aim of image enhancement is to reconstruct the true image from the corrupted image.
Image degradation can be due to the addition of different types of noise in the original image.
Impulse noise generates pixels with gray value not consistent with their local neighbourhood.
arXiv Detail & Related papers (2022-04-20T08:23:52Z) - Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters [151.2423480789271]
A novel pruning method, termed CLR-RNF, is proposed for filter-level network pruning.
We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts.
arXiv Detail & Related papers (2022-02-15T04:53:24Z) - 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) - 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) - Quarter Laplacian Filter for Edge Aware Image Processing [32.885698849515045]
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.
arXiv Detail & Related papers (2021-01-20T02:29:54Z) - Data Agnostic Filter Gating for Efficient Deep Networks [72.4615632234314]
Current filter pruning methods mainly leverage feature maps to generate important scores for filters and prune those with smaller scores.
In this paper, we propose a data filter pruning method that uses an auxiliary network named Dagger module to induce pruning.
In addition, to help prune filters with certain FLOPs constraints, we leverage an explicit FLOPs-aware regularization to directly promote pruning filters toward target FLOPs.
arXiv Detail & Related papers (2020-10-28T15:26:40Z) - 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) - Filter Grafting for Deep Neural Networks: Reason, Method, and
Cultivation [86.91324735966766]
Filter is the key component in modern convolutional neural networks (CNNs)
In this paper, we introduce filter grafting (textbfMethod) to achieve this goal.
We develop a novel criterion to measure the information of filters and an adaptive weighting strategy to balance the grafted information among networks.
arXiv Detail & Related papers (2020-04-26T08:36:26Z)
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.