This paper describes a recently created image database, TID2013, intended for evaluation of full-reference visual quality assessment metrics. With respect to TID2008, the new database contains a larger number (3000) of test images obtained from 25 reference images, 24 types of distortions for each reference image, and 5 levels for each type of distortion. Motivations for introducing 7 new types of distortions and one additional level of distortions are given; examples of distorted images are presented. Mean opinion scores (MOS) for the new database have been collected by performing 985 subjective experiments with volunteers (observers) from five countries (Finland, France, Italy, Ukraine, and USA). The availability of MOS allows the use of the designed database as a fundamental tool for assessing the effectiveness of visual quality. Furthermore, existing visual quality metrics have been tested with the proposed database and the collected results have been analyzed using rank order correlation coefficients between MOS and considered metrics. These correlation indices have been obtained both considering the full set of distorted images and specific image subsets, for highlighting advantages and drawbacks of existing, state of the art, quality metrics. Approaches to thorough performance analysis for a given metric are presented to detect practical situations or distortion types for which this metric is not adequate enough to human perception. The created image database and the collected MOS values are freely available for downloading and utilization for scientific purposes. We have created a new large database.This database contains larger number of distorted images and distortion types.MOS values for all images are obtained and provided.Analysis of correlation between MOS and a wide set of existing metrics is carried out.Methodology for determining drawbacks of existing visual quality metrics is described.

https://hal.archives-ouvertes.fr/hal-01109219/document

Image database TID2013

https://eprints.whiterose.ac.uk/43377/2/WRRO_43377.pdf

The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle

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Spotlight Synthetic Aperture Radar Signal Processing Algorithms

Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: A review

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2014.0311

Simultaneous image fusion and denoising with adaptive sparse representation

Two challenges have been faced in signal processing of ultrahigh-resolution spaceborne synthetic aperture radar (SAR). The first challenge is constructing a precise range model, and the second one is to develop an efficient imaging algorithm since traditional algorithms fail to process ultrahigh-resolution spaceborne SAR data effectively. In this paper, a novel high-order imaging algorithm for high-resolution spaceborne SAR is presented. First, a modified equivalent squint range model (MESRM) is developed by introducing equivalent radar acceleration into the equivalent squint range model, and it is more suitable for high-resolution spaceborne SAR. The signal model based on the MESRM is also presented. Second, a novel high-order imaging algorithm is derived. The insufficient pulse-repetition frequency problem is solved by an improved subaperture method, and accurate focusing is achieved through an extended hybrid correlation algorithm. Simulations are performed to validate the presented algorithm.

/pdf/a-high-order-imaging-algorithm-for-high-resolution-51li6te63j.pdf

A High-Order Imaging Algorithm for High-Resolution Spaceborne SAR Based on a Modified Equivalent Squint Range Model

Spaceborne polarimetric synthetic aperture radar systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR). A new set of FR estimators is derived from the off-diagonal terms in the measured covariance matrix of a distributed target. These estimators have a phase ambiguity of period , instead of /2 as for the published estimators, and this ambiguity can be completely resolved for arbitrarily large values of FR using total electron content maps derived from Global Navigation Satellite System measurements. Simulations show that one of the new estimators has particularly high resistance to system noise and channel amplitude imbalance but greater sensitivity to channel phase imbalance than the published estimators. Hence, the expected values of residual system distortion after calibration may affect the choice of estimator.

Improved Estimators of Faraday Rotation in Spaceborne Polarimetric SAR Data

Spaceborne synthetic aperture radar (SAR) systems operating at lower frequencies, such as P-band, are significantly affected by Faraday rotation (FR) effects. A novel algorithm for calibrating the circular-transmit-and-linear-receive (CTLR) mode spaceborne compact polarimetric SAR using mixed calibrators is proposed, which is able to correct precisely both FR and radar system errors (i.e., channel imbalance and crosstalk). Six sets of mixed calibrators, consisting of both passive calibrators and polarimetric active radar calibrators (PARCs), are investigated. Theoretical analysis and simulations demonstrate that the optimal calibration scheme combines four polarimetric selective mixed calibrators, including two gridded trihedrals and two PARCs, together with total-electron-content measurements by the Global Navigation Satellite System system.

/pdf/calibration-of-spaceborne-ctlr-compact-polarimetric-low-2tlnbp90lr.pdf

Calibration of Spaceborne CTLR Compact Polarimetric Low-Frequency SAR Using Mixed Radar Calibrators

High-resolution wide-swath (HRWS) imaging with space- borne synthetic aperture radar (SAR) can be achieved by using az- imuth displacement phase center antenna (DPCA) technique How- ever, it will consequently leads to extremely high data rate on satellite downlink system A novel sparse sampling scheme based on compressed sensing (CS) theory for azimuth DPCA SAR was proposed, by which only a small proportion of radar echoes are utilized for imaging to re- duce data rate The corresponding image formation algorithm for the proposed scheme was presented in the paper The SAR echo signal of each channel can be reconstructed with high probability by using orthogonal matching pursuit (OMP) algorithm in Doppler frequency domain The reconstructed echo signals of each channel are jointly processed by means of spectrum reconstructing fllter for compensat- ing Doppler spectrum aliasing resulting from non-uniform sampling in azimuth direction The high quality SAR image can be obtained by us- ing chirp scaling algorithm The efiectiveness of the proposed approach was validated by computer simulations using both point targets and distributed targets

https://www.jpier.org/PIER/pier125/28.11121101.pdf

A Novel Image Formation Algorithm for High-Resolution Wide-Swath Spaceborne SAR Using Compressed Sensing on Azimuth Displacement Phase Center Antenna

A despeckling technique based on multiple image reconstruction and selective 3-dimensional flltering is proposed. Multiple SAR images are reconstructed from a single SAR image by employing compressive sensing (CS) theory. In order to obtain multiple images from single SAR image, multiple subsets of pixels are selected from input SAR image by imposing restriction that each subset has at least 20% difierent pixels from any other subset. These subsets are taken as measurement vectors in CS framework to obtain multiple SAR images. A despeckled image is obtained by employing selective 3-dimensional flltering to multiple reconstructed SAR image. The proposed technique is tested on single look complex TerraSAT-X data set, and experimental results exhibit that the proposed technique outperformed benchmark despekling methods in terms of visual quality and despeckling quality metrics.

/pdf/sar-image-despeckling-by-selective-3d-filtering-of-multiple-3lgxxar74p.pdf

Jie Chen

Papers

A High-Order Imaging Algorithm for High-Resolution Spaceborne SAR Based on a Modified Equivalent Squint Range Model

Improved Estimators of Faraday Rotation in Spaceborne Polarimetric SAR Data

Calibration of Spaceborne CTLR Compact Polarimetric Low-Frequency SAR Using Mixed Radar Calibrators

A Novel Image Formation Algorithm for High-Resolution Wide-Swath Spaceborne SAR Using Compressed Sensing on Azimuth Displacement Phase Center Antenna

SAR Image Despeckling by Selective 3D Filtering of Multiple Compressive Reconstructed Images