High Contrast Scene Research Articles

An Antagonistic Photovoltaic Memristor for Bioinspired Active Contrast Adaptation.

Machine vision systems that consist of cameras and image-processing components for visual inspection and identification tasks play a critical role in various intelligent applications, including pilotless vehicles and surveillance systems. However, current systems usually possess a limited dynamic range and fixed photoresponsivity, restricting their capability of gaining high-fidelity images when encoding a high-contrast scene. Here, it is shown that a photovoltaic memristor incorporating two antagonistic photovoltaic junctions can autonomously adjust its response to varying light stimuli, enabling the amplification of shadows and inhibition of highlight saturation. Due to the dynamic photodoping effect at the p-n junction with an asymmetrical profile, the photocurrent polarities of the antagonistic memristor can be changed as the light intensity increases. The light-intensity-dependent switchable photovoltaic behaviors match Weber's law where photosensitivity is inversely proportional to the light stimuli. An 11×11 memristor array is used to detect a high-contrast scene with light intensities ranging from 1 to 5×104 µW cm-2, achieving a similar active contrast adaptation performance compared with the human visual systems (less than 1.2 s at 94 dB). This work paves the way for innovative neuromorphic device designs and may lead to the development of state-of-the-art active visual adaptation photosensors.

Polarized computational ghost imaging in scattering system with half-cyclic sinusoidal patterns

Computational Ghost Imaging (CGI) can reconstruct the scene images through the correlation algorithm between the preset illumination patterns and the intensities of the detection bucket detector. In the actual transmission process, the scattering system in the imaging path can affect the imaging quality of the CGI. The half-cyclic sinusoidal-pattern based CGI (HCSP-CGI) can recover the scene images at low sampling rates (SRs). Based on the Monte Carlo (MC) model, we propose and construct a reflective polarization system based the HCSP-CGI in fog environment and put forward a method that combines polarization information with the HCSP-CGI called the PHCSP-CGI. When the difference in reflectivity between target and background is small, the difference in polarization characteristics between target and background can help CGI to remove the interference of stray light. The imaging performance of the PHCSP-CGI at different SRs and scattering distances (SDs) are analyzed. Experimental results show that high-contrast scene can still be obtained, providing new applications for object recognition in scattering system.

Estimating ground fractional vegetation cover using the double-exposure method

Fractional vegetation cover (FVC) is a key parameter in ecological models. It is important to determine the ground FVC quickly and accurately in studies of soil erosion, surface energy balance, and carbon cycling. As one of the FVC ground measurement methods, the photographic method is easy to operate with relatively high precision. However, its classification result showed poor accuracy when an image of a high-contrast scene contained a shadow region where a low signal-to-noise ratio (SNR) existed, because the single-exposure image in the photographic method did not contain sufficient surface information about both the illuminated and shadowed parts. This article presents application of a double-exposure photographic method to determine vegetation cover in the shadow region of an image. It consists of two measurements used in acquiring images (normal and over-exposure) and one image-processing part to handle the obtained images. Illuminated vegetation and soil, as well as the shadow region, was classified with the normally exposed image in the intensity, hue, and saturation (IHS) colour space, and the shadow region was further classified as shadowed vegetation and shadowed soil using the over-exposed image. The results indicate that the over-exposed image reduced the average bias of the FVC in the shadow region from 15.40% to −4.14% and the root mean square error (RMSE) from 0.174 to 0.066. The RMSE of the entire scene was 0.055 in the over-exposed image and 0.092 in the single-exposed image. The double-exposure method also showed a better classification result than the high dynamic range method in deep shadow regions. This study shows that this method is capable of distinguishing vegetation and soil in the shadow region and thus it is an effective and accurate method for ground FVC measurement.

Layered‐based exposure fusion algorithm

Owing to the limitation of dynamic range, a single still image is usually insufficient to describe a high contrast scene. Fusing multi-exposure images of the same scene can produce a resulting image with details both in the bright and the dark regions. However, they may be sensitive to the exposure parameters of the input images. In this study, a global layer is introduced to improve the robustness of the fusion method. The global layer is employed to preserve the overall luminance of a real scene and avoid possible luminance reversion artefacts. Then, details are recovered in the gradient domain by a Poisson solver. Experimental results show the superior performance of our approach in terms of robustness and details preservation.

SAR Doppler Ambiguity Resolver Based on Entropy Minimization

The determination of Doppler ambiguity number (DAN) is indispensable for the generation of high-quality synthetic aperture radar (SAR) images. An incorrect DAN leads to lower image signal-to-noise ratio and degradation in the system impulse response function. Based on the relationship between DAN errors and the image quality, a novel DAN estimation algorithm named image-quality-based Doppler ambiguity resolver is presented. In the proposed algorithm, the DAN-dependent linear range cell migration correction and DAN-dependent azimuth compression are carried out in subscenes to obtain 2-D compressed SAR images, which are further employed to estimate DAN via minimizing the entropy. The presented algorithm is more robust than conventional methods to cope with demands of both low- and high-contrast scene applications. In addition, the approach is computationally efficient because it can be properly applied to segments of range-compressed data in small size and only a few sets of short fast Fourier transforms are required. Finally, experiments over real data of airborne X-band and spaceborne C-band are carried out to demonstrate the performance of the proposed approach.

An evaluation of image reproduction algorithms for high contrast scenes on large and small screen display devices

Rendering high contrast scenes on display devices with limited dynamic range is a challenging task. Two groups of algorithms have emerged to take up this challenge: tone mapping operators (TMOs) and more recently exposure fusion (EF) techniques. While several formal evaluation studies comparing TMOs exist, no formal evaluation has yet been performed that compares EF techniques with each other or compares them against TMOs. Moreover, with the advancements in hand-held devices and programmable digital cameras it became possible to directly capture and view high dynamic range (HDR) content on these devices which are characterized by their small screens. However, currently very little is known about how to best visualize a high contrast scene on a small screen. Thus the primary goal of this paper is to provide answers to both of these questions by conducting a series of rigorous psychophysical experiments. Our results suggest that the best tone mapping algorithms are generally superior to EF algorithms except for the reproduction of colors. Furthermore, contrary to some previous work, we find that the differences between algorithms are barely perceptible on small screens and therefore one can opt for a simpler solution than a more complex and accurate one.

RF Nonlinearities in an Analog Optical Link and Their Effect on Radars Carrying Linear and Nonlinear Frequency Modulated Pulses

Optical links are afflicted with RF nonlinearities, originating from various opto-electronic devices, like intensity modulators. The effects of such nonlinearities on nonlinear frequency modulated (NLFM) pulses are studied in detail for both clutter and discrete returns and compared to the linear frequency modulated case. While for discrete returns, NLFM coding generates weaker ghosts, this advantage is lost for clutter, where both codings exhibit similar performance. When such an optical link in a photonic beamformer processes the signal returned from a high contrast scene, third and higher order nonlinearities severely limit the obtainable contrast of the imaging radar. For third-order nonlinearities and high RF input powers, an inverse fourth-order relationship exists between the contrast and the input voltage. For lower inputs, the contrast is initially limited by system noise and then by the skirts of the compressed impulse response. These results for the two frequency codings are experimentally confirmed in a high frequency (10 GHz), wideband (1 GHz) photonic link, where the nonlinearities originate from the behavior of a Mach-Zehnder intensity modulator. Specifically, to achieve a contrast better than 25 dB, the RF input to the Mach-Zehnder modulator should not exceed 1/8 of the link input third-order intercept voltage.