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In most large field of view (FOV) observations, the distortion problem is inevitably and significantly more serious than in small FOV ones. In the circumstances, many traditional star ident...

Haze degrades visual information of remotely sensed images. Therefore, haze removal is a demanding and significant task for visual multispectral information improvement. The existing haze removal techniques utilize different restrictions and before restoring hazy images in an efficient manner. The review of existing haze removal methods demonstrates that the haze-free images suffer from colour distortion and halo artefacts problems. To solve these issues, an improved restoration model based dark channel prior is proposed in this paper. The proposed technique has redefined transmission map, with the aim to reduce the colour distortion problem. The modified joint trilateral filter is also utilized to improve the coarse estimated atmospheric veil. The experimental results reveal that the proposed approach provides visually significant haze-free images and also preserves the significant detail.

Shipborne bistatic inverse synthetic aperture radar (ISAR), with a stationary and visible transmitter on land and a mobile and stealthy receiver on a ship with complex 3-D rotations is a strategic asset which can provide complementary information to monostatic ISAR. However, it must first overcome the serious problems of defocusing and distortion due to the time-varying bistatic angle. In this letter, an improved cubic chirplet decomposition method is proposed to solve the defocusing problem. By decomposing the ISAR echo into cubic chirplet atoms and estimating the parameters for each term, higher-order defocusing terms can be eliminated. Moreover, a novel method based on linked scatterers is put forward to solve the distortion problem. The method utilizes ‘linked scatterers’ from monostatic ISAR image to calculate and compensate the distortion angle. The effectiveness of the proposed methods is verified by the simulation results.

Thin-wall injection molding parts have the shrinkage distortion problem, this is mainly caused by the setting of processing parameters. Gray relational analysis and Taguchi method were applied to obtain the combination of processing parameters that could produce the product with optimal multiple quality characteristics. We used gray relational analysis to solve the single quality characteristic problem of Taguchi method and used the response graph of gray relational analysis to receive the optimal processing parameters combinations, which would produce the product with multiple quality characteristics. Finally, the confirmation experiment proved the reliability and reproducibility of this experiment with 95% confidence interval.

Image fusion is a technique that is used to combine the spatial structure of a high-resolution panchromatic image with the spectral information of a low- resolution multispectral image to produce a high-resolution multispectral image. Currently, image fusion techniques via color or statistical transforms such as the Intensity-Hue-Saturation (IHS) and principal component (PC) methods are still widely used. These methods create multispectral images of higher spatial resolution but usually at the cost of color distortions in the fused images. This is especially true if the wavelength range of the panchromatic image does not correspond to that of the employed multispectral bands or for multitemporal/multisensoral fusion. To overcome the color distortion problem, a number of new fusion methods have been developed over the last years. One of these is the Ehlers fusion algorithm, which is based on an IHS transform coupled with adaptive filtering in the Fourier domain. This method preserves the spectral character...

The objective of this study is to derive the time scale to be applied to bed‐load transport in movable‐bed experiments. Based on the model similitude law, the hydraulic time scale and the sedimentation time scales, which were derived from nine selected empirical formulas, were compared in detail. The hydraulic time scale is found to usually be smaller than the sedimentation time scale in a length‐scale distorted model. The time‐scale distortion problem can only be solved using a model that employs heavy (more dense) sediment, which is contrary to the general movablebed models that employ light (less dense) sediment. The analytical results show that the deviation between these two time scales increases as the model length‐scale distortion ratio increases. The time‐scale distortion problem may only be negligible when the model has a small length‐scale distortion ratio.

The size distortion problem is clearly indicative of the small-sample approximation in the Markov-switching regression model. This paper shows that the bootstrap procedure can relieve the effects that this problem has. Our Monte Carlo simulation results reveal that the bootstrap maximum likelihood asymptotic approximations to the distribution can often be good when the sample size is small.