Blind restoration for nonuniform aerial images using nonlocal Retinex model and shearlet-based higher-order regularization

  title={Blind restoration for nonuniform aerial images using nonlocal Retinex model and shearlet-based higher-order regularization},
  author={R. Chen and Huizhu Jia and Xiaodong Xie and Wen Gao},
  journal={Journal of Electronic Imaging},
Abstract. Aerial images are often degraded by space-varying motion blurs and simultaneous uneven illumination. To recover a high-quality aerial image from its nonuniform version, we propose a patchwise restoration approach based on a key observation that the degree of blurring is inevitably affected by the illumination conditions. A nonlocal Retinex model is developed to accurately estimate the reflectance component from the degraded aerial image. Thereafter, the uneven illumination is… 
5 Citations

Fraction-Order Total Variation Image Blind Restoration Based on Self-Similarity Features

An effective image blind restoration method using self-similarity as prior information is proposed for restoring the blurry images and it is found that natural images usually exhibit some texture features, which is a popular texture features and well-defined in the statistics.

Abnormal crowd density estimation in aerial images

This work adapted the bag of words technique using the multiblock local binary pattern as a texture descriptor to extract low-level features in aerial images and used a three-level classification strategy to reduce confusion between crowd density classes.

Single image super-resolution using deep hierarchical attention network

A compact and accurate super-resolution algorithm using the attention-augmented convolutional neural network, which can exploit and weight hierarchical features at multiple scales and levels to improve learning capability is presented.



Blind restoration of aerial imagery degraded by spatially varying motion blur

This paper develops a methodology for blind restoration of spatially varying blur induced by camera motion caused by instabilities of the moving platform and proposes a scheme to estimate the original focused image affected by arbitrarily-shaped blur kernels.

Comparative and Quantitative Study of Fundamental Approaches on Digital Aerial Image Deblurring

This article applies several fundamental methods to aerial image deblurring to evaluate the outcomes in an objective manner, where metrics of discrete entropy, relative entropy, discrete energy, mutual information, contrast and homogeneity are introduced for evaluating aerial imagedeblurring.

Removing Atmospheric Turbulence via Space-Invariant Deconvolution

An approach capable of restoring a single high-quality image from a given image sequence distorted by atmospheric turbulence is proposed, which reduces the space and time-varying deblurring problem to a shift invariant one.

Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior

A robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations, which can recover a high quality image from a set of observations containing potentially both blurry and noisy examples, without knowing a priori the degradation type of each observation.

Blind Image Deblurring Using Spectral Properties of Convolution Operators

It is shown that the blurry image itself actually encodes rich information about the blur kernel, and such information can indeed be found by exploring and utilizing a well-known phenomenon, that is, sharp images are often high pass, whereas blurry images are usually low pass.

Image Deblurring with Blurred / Noisy Image Pairs

Photos taken under dim lighting conditions by a handheld camera are usually either too noisy or too blurry. In our project, we implement an image deblurring method that takes advantage of a pair of

Efficient Patch-Wise Non-Uniform Deblurring for a Single Image

This work employs the total variation (TV) regularization to recover a latent image, in which the edges are better enhanced and the ringing artifacts are reduced, rather than Tikhonov regularization that previous algorithms adopt, and can be estimated more accurately from the latent image and estimated in a closed form while previous methods cannot estimate kernels in closed forms.

Non-Uniform Deblurring in HDR Image Reconstruction

A method is developed that takes input non-uniformly blurred and differently exposed images to extract the deblurred, latent irradiance image and estimates the TSFs of the blurred images from locally derived point spread functions by exploiting their linear relationship.

A New Detail-Preserving Regularization Scheme

A novel regularization model is proposed that integrates two recently developed regularization tools: total generalized variation (TGV) by Bredies, Kunisch, and Pock; and shearlet transform by Labate, Lim, Kutyniok, and Weiss and “selectively regularizes” different image regions at different levels and thus largely avoids oil painting artifacts.

High-quality motion deblurring from a single image

A new algorithm for removing motion blur from a single image is presented using a unified probabilistic model of both blur kernel estimation and unblurred image restoration and is able to produce high quality deblurred results in low computation time.