234 research outputs found
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach
The bilateral filter is known to be quite effective in denoising images
corrupted with small dosages of additive Gaussian noise. The denoising
performance of the filter, however, is known to degrade quickly with the
increase in noise level. Several adaptations of the filter have been proposed
in the literature to address this shortcoming, but often at a substantial
computational overhead. In this paper, we report a simple pre-processing step
that can substantially improve the denoising performance of the bilateral
filter, at almost no additional cost. The modified filter is designed to be
robust at large noise levels, and often tends to perform poorly below a certain
noise threshold. To get the best of the original and the modified filter, we
propose to combine them in a weighted fashion, where the weights are chosen to
minimize (a surrogate of) the oracle mean-squared-error (MSE). The
optimally-weighted filter is thus guaranteed to perform better than either of
the component filters in terms of the MSE, at all noise levels. We also provide
a fast algorithm for the weighted filtering. Visual and quantitative denoising
results on standard test images are reported which demonstrate that the
improvement over the original filter is significant both visually and in terms
of PSNR. Moreover, the denoising performance of the optimally-weighted
bilateral filter is competitive with the computation-intensive non-local means
filter.Comment: To appear in the IEEE International Conference on Image Processing
(ICIP 2015). Link to the Matlab code added in the revisio
Image denoising using optimally weighted bilateral filters: A sure and fast approach
The bilateral filter is known to be quite effective in denoising images corrupted with small dosages of additive Gaussian noise. The denoising performance of the filter, however, is known to degrade quickly with the increase in noise level. Several adaptations of the filter have been proposed in the literature to address this shortcoming, but often at a substantial computational overhead. In this paper, we report a simple pre-processing step that can substantially improve the denoising performance of the bilateral filter, at almost no additional cost. The modified filter is designed to be robust at large noise levels, and often tends to perform poorly below a certain noise threshold. To get the best of the original and the modified filter, we propose to combine them in a weighted fashion, where the weights are chosen to minimize (a surrogate of) the oracle mean-squared-error (MSE). The optimally-weighted filter is thus guaranteed to perform better than either of the component filters in terms of the MSE, at all noise levels. We also provide a fast algorithm for the weighted filtering. Visual and quantitative denoising results on standard test images are reported which demonstrate that the improvement over the original filter is significant both visually and in terms of PSNR. Moreover, the denoising performance of the optimally-weighted bilateral filter is competitive with the computation-intensive non-local means filter
A multiresolution framework for local similarity based image denoising
In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing
This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pixelRL for various image processing applications. In
pixelRL, each pixel has an agent, and the agent changes the pixel value by
taking an action. We also propose an effective learning method for pixelRL that
significantly improves the performance by considering not only the future
states of the own pixel but also those of the neighbor pixels. The proposed
method can be applied to some image processing tasks that require pixel-wise
manipulations, where deep RL has never been applied. We apply the proposed
method to three image processing tasks: image denoising, image restoration, and
local color enhancement. Our experimental results demonstrate that the proposed
method achieves comparable or better performance, compared with the
state-of-the-art methods based on supervised learning.Comment: Accepted to AAAI 201
Satellite Image Denoising Using Local Spayed and Optimized Center Pixel Weights
Now a day’s digital image processing applications are widely used in various fields such as medical, military, satellite, remote sensing and even web applications also. In any application image denoising is a challenging task because noise removal will increase the digital quality of an image and will improve the perceptual visual quality. In this paper we proposed a new method “local spayed and optimized center pixel weights (LSOCPW) with non local means” to improve the denoising performance of digital color image sequences. Simulation results show that the proposed method has given the better performance when compared to the existing algorithms in terms of peak signal to noise ratio (PSNR) and mean square error (MSE).DOI:http://dx.doi.org/10.11591/ijece.v4i5.662
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