General Participative Media Single Image Restoration
نویسندگان
چکیده
This paper describes a method to restore degraded images captured in general participative media — fog, turbid water, sand storm, etc. To obtain generality, we, first, propose a novel interpretation of the participative media image formation by considering the color variation of the media. Second, we introduce that joining different image priors is an effective alternative for image restoration. The proposed method contains a Composite Prior supported by statistics collected on both haze-free and degraded participative environment images. The key of the method is joining two complementary measures — local contrast and color. The results presented for a variety of underwater and haze images demonstrate the power of the method. Moreover, we showed the potential of our method using a special dataset for which a reference haze-free image is available for comparison.
منابع مشابه
Image Restoration Using A PDE-Based Approach
Image restoration is an essential preprocessing step for many image analysis applications. In any image restoration techniques, keeping structure of the image unchanged is very important. Such structure in an image often corresponds to the region discontinuities and edges. The techniques based on partial differential equations, such as the heat equations, are receiving considerable attention i...
متن کاملImage Restoration by Variable Splitting based on Total Variant Regularizer
The aim of image restoration is to obtain a higher quality desired image from a degraded image. In this strategy, an image inpainting method fills the degraded or lost area of the image by appropriate information. This is performed in such a way so that the obtained image is undistinguishable for a casual person who is unfamiliar with the original image. In this paper, different images are degr...
متن کاملDiscriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and...
متن کاملA Deep Model for Super-resolution Enhancement from a Single Image
This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...
متن کاملMRL-filters: a general class of nonlinear systems and their optimal design for image processing
A class of morphological/rank/linear (MRL)-filters is presented as a general nonlinear tool for image processing. They consist of a linear combination between a morphological/rank filter and a linear filter. A gradient steepest descent method is proposed to optimally design these filters, using the averaged least mean squares (LMS) algorithm. The filter design is viewed as a learning process, a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- CoRR
دوره abs/1603.01864 شماره
صفحات -
تاریخ انتشار 2016