9,642 research outputs found
Removing Atmospheric Noise Using Channel Selective Processing For Visual Correction
In the presented paper; we propose an effective image fog removal technique with a color stabilization technique which is a total 2-level process for image restoration with a HSI (Hue Saturation Intensity) based evaluation process. The approach uses extraction of suppressed pixels from an RGB image affected by smoke, steam, fog which is form of white and Gaussian noise. From our observation of most images in fog environment contain some pixels which have low values of luminescence in every color channel (considering RGB image).Using this model, we can directly estimate the effective density of fog and recover the most affected parts in the image. The parameter of calculating the effective luminescence which is a form of intensity, and also gives the scattering estimates of the light, the combined Laplace of the luminescence-light and suppressed pixels values gives us the basic map of light spread which is further used in the restoration of intensity. The transmission of intensity between the calculated fog values in the image give the estimate for the local transition between the intensity values and color values. This factor helps in the color restoration of the affected image and estimates the proper restoration of image after removal of dense fog particles. After the removal of fog particles, we then restore the color balance in the image using an auto-color-contrast stabilization technique. This is the 2-level fog restoration method. The visibility is highly dependent on the saturation of color values and not over saturation, which accounts for image quality improvements. In order to evaluate in-depth the effectiveness, we have also introduced the HSI mapping of the images, as this will show the true restoration of intensity and saturation in the fog image. Results on various images demonstrate the power of the proposed algorithm. To measure the efficiency of the algorithm the parameter of visual index is also estimated which further evaluates the robustness of the proposed algorithm for the HVS (Human Visual System) for the de-fogged images
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Recent developments in deep learning have revolutionized the paradigm of
image restoration. However, its applications on real image denoising are still
limited, due to its sensitivity to training data and the complex nature of real
image noise. In this work, we combine the robustness merit of model-based
approaches and the learning power of data-driven approaches for real image
denoising. Specifically, by integrating graph Laplacian regularization as a
trainable module into a deep learning framework, we are less susceptible to
overfitting than pure CNN-based approaches, achieving higher robustness to
small datasets and cross-domain denoising. First, a sparse neighborhood graph
is built from the output of a convolutional neural network (CNN). Then the
image is restored by solving an unconstrained quadratic programming problem,
using a corresponding graph Laplacian regularizer as a prior term. The proposed
restoration pipeline is fully differentiable and hence can be end-to-end
trained. Experimental results demonstrate that our work is less prone to
overfitting given small training data. It is also endowed with strong
cross-domain generalization power, outperforming the state-of-the-art
approaches by a remarkable margin
Variational Image Segmentation Model Coupled with Image Restoration Achievements
Image segmentation and image restoration are two important topics in image
processing with great achievements. In this paper, we propose a new multiphase
segmentation model by combining image restoration and image segmentation
models. Utilizing image restoration aspects, the proposed segmentation model
can effectively and robustly tackle high noisy images, blurry images, images
with missing pixels, and vector-valued images. In particular, one of the most
important segmentation models, the piecewise constant Mumford-Shah model, can
be extended easily in this way to segment gray and vector-valued images
corrupted for example by noise, blur or missing pixels after coupling a new
data fidelity term which comes from image restoration topics. It can be solved
efficiently using the alternating minimization algorithm, and we prove the
convergence of this algorithm with three variables under mild condition.
Experiments on many synthetic and real-world images demonstrate that our method
gives better segmentation results in comparison to others state-of-the-art
segmentation models especially for blurry images and images with missing pixels
values.Comment: 23 page
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