Low Contrast Scenes Research Articles

Low-contrast scene feature-based infrared nonuniformity correction method for airborne target detection

The fixed mode noise (FPN) caused by the nonuniform response of the infrared focal plane array (IRFPA) will inevitably affect the image quality, so the nonuniformity correction (NUC) method is needed to eliminate such noise. Most of the traditional NUC methods are suitable for high-contrast scenes but do not work as well in low-contrast scenes such as sky and water. Therefore, this paper proposes an infrared NUC method based on feature extraction and image registration for low-contrast scenes and combines the Kalman filter to accelerate the convergence speed, which achieves a good NUC effect. A threshold gating mechanism of learning rate is proposed to eliminate the influence of discrete defective pixels without losing the details of the target. In simulation, the proposed method can achieve high-precision image registration in low-contrast scenes, and the registration error can be less than 0.5 pixels under the condition of a signal-to-noise ratio (SNR) of 3. The experimental results of real infrared images show that the average roughness index of 0.0563 and the target SNR of 7.99 can be obtained after correcting the real infrared image with the proposed method, both of which are better than other methods, thus verifying the effectiveness and superiority of the proposed method.

A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery.

It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (), to the Lucas–Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.

Material identification of construction machinery based on multisource sensor information fusion

In order to improve the recognition accuracy of construction machinery and equipment and materials in low contrast scenes, a construction machinery material recognition algorithm based on multisource sensor information fusion is proposed. In the paper, the millimeter wave radar is fused with the camera considering its strong penetration ability in rainy and foggy days and dim environments. Firstly, the spatial coordinates of radar and camera are unified by establishing a spatial fusion model of millimeter wave and camera; then the target acquired by millimeter wave is projected onto the image and the detection frame intersection and ratio model is used to generate the region of interest of the camera; finally, the improved YOLOv2 algorithm is used to identify the region of interest, and in the improved idea, the low-level information is first connected with the high-level information in multilayer depth. At the same time, a multiscale feature pyramid network structure is used to achieve recognition of objects of different scales. This model effectively reduces interference from other feature categories while improving the recognition efficiency of the system. The algorithm can effectively improve the recognition accuracy of mechanical materials in low-contrast scenes, as demonstrated by the validation of different scenes.

Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.

Ship Detection in Multispectral Remote Sensing Images via Saliency Analysis

Despite the increasing visible optical remote sensing cameras equipped with panchromatic and four-band multispectral sensors, the application of multispectral data is still rarely used in the field of remote sensing ship target detection with the openly recognized challenge to improve detection precision. Towards this end, a ship target saliency detection method is proposed in the paper, which combines weighted least squares (WLS) with maximum symmetric surround (MSS), based on RGB-NIR four-band multispectral remote sensing images to locate the candidate area of ship targets quickly and accurately. The high frequency information of the NIR band image is extracted through the WLS filter and integrated into the RGB band image, and then the saliency of the image is analyzed. The detection results are combined with AIS to achieve complementary information for ship recognition. Some experiments show that the proposed method can effectively suppress the complex background information of clutter interference such as cloud waves and sea waves, highlight the ship target in low contrast scenes, and also increase recall and precision effectively.

A Real-Time Vehicle Detection System under Various Bad Weather Conditions Based on a Deep Learning Model without Retraining.

Numerous vehicle detection methods have been proposed to obtain trustworthy traffic data for the development of intelligent traffic systems. Most of these methods perform sufficiently well under common scenarios, such as sunny or cloudy days; however, the detection accuracy drastically decreases under various bad weather conditions, such as rainy days or days with glare, which normally happens during sunset. This study proposes a vehicle detection system with a visibility complementation module that improves detection accuracy under various bad weather conditions. Furthermore, the proposed system can be implemented without retraining the deep learning models for object detection under different weather conditions. The complementation of the visibility was obtained through the use of a dark channel prior and a convolutional encoder–decoder deep learning network with dual residual blocks to resolve different effects from different bad weather conditions. We validated our system on multiple surveillance videos by detecting vehicles with the You Only Look Once (YOLOv3) deep learning model and demonstrated that the computational time of our system could reach 30 fps on average; moreover, the accuracy increased not only by nearly 5% under low-contrast scene conditions but also 50% under rainy scene conditions. The results of our demonstrations indicate that our approach is able to detect vehicles under various bad weather conditions without the need to retrain a new model.

Heterogeneous Temporal Contrast Adaptation in Drosophila Direction-Selective Circuits.

In visual systems, neurons adapt both to the mean light level and to the range of light levels, or the contrast. Contrast adaptation has been studied extensively, but it remains unclear how it is distributed among neurons in connected circuits, and how early adaptation affects subsequent computations. Here, we investigated temporal contrast adaptation in neurons across Drosophila's visual motion circuitry. Several ON-pathway neurons showed strong adaptation to changes in contrast over time. One of these neurons, Mi1, showed almost complete adaptation on fast timescales, and experiments ruled out several potential mechanisms for its adaptive properties. When contrast adaptation reduced the gain in ON-pathway cells, it was accompanied by decreased motion responses in downstream direction-selective cells. Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes. The benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits.

Fourier Single-Pixel Imaging Based on Lateral Inhibition for Low-Contrast Scenes

A Fourier single-pixel imaging technique based on lateral inhibition is proposed to improve the reconstruction contrast and quality for low-contrast scenes. The mathematical models are developed, and corresponding simulations and experiments are conducted to verify and evaluate the features. Results show that this strategy strengthens the image contrast and structural similarity index mainly under low sampling ratios compared with the traditional Fourier single-pixel imaging. It offers an optional solution for various Fourier single-pixel imaging applications at low sampling ratios for low-contrast scenes.

Achieving high-resolution thermal imagery in low-contrast lake surface waters by aerial remote sensing and image registration

A two-platform measurement system for realizing airborne thermography of the Lake Surface Water Temperature (LSWT) with ~0.8 m pixel resolution (sub-pixel satellite scale) is presented. It consists of a tethered Balloon Launched Imaging and Monitoring Platform (BLIMP) that records LSWT images and an autonomously operating catamaran (called ZiviCat) that measures in situ surface/near surface temperatures within the image area, thus permitting simultaneous ground-truthing of the BLIMP data. The BLIMP was equipped with an uncooled InfraRed (IR) camera. The ZiviCat was designed to measure along predefined trajectories on a lake. Since LSWT spatial variability in each image is expected to be low, a poor estimation of the common spatial and temporal noise of the IR camera (nonuniformity and shutter-based drift, respectively) leads to errors in the thermal maps obtained. Nonuniformity was corrected by applying a pixelwise two-point linear correction method based on laboratory experiments. A Probability Density Function (PDF) matching in regions of overlap between sequential images was used for the drift correction. A feature matching-based algorithm, combining blob and region detectors, was implemented to create composite thermal images, and a mean value of the overlapped images at each location was considered as a representative value of that pixel in the final map. The results indicate that a high overlapping field of view (~95%) is essential for image fusion and noise reduction over such low-contrast scenes. The in situ temperatures measured by the ZiviCat were then used for the radiometric calibration. This resulted in the generation of LSWT maps at sub-pixel satellite scale resolution that revealed spatial LSWT variability, organized in narrow streaks hundreds of meters long and coherent patches of different size, with unprecedented detail.

Robust Localization of Texts in Real-World Images

Localization of texts in natural images could be an important stage in many applications such as content-based image retrieval, visual impairment assistance systems, automatic robot navigation in urban environments and tourist assistance systems. However due to the variations of font, script, scale, orientations, color, shadow and lighting conditions, robust scene text localization is still a challenging task. In this paper, we propose a novel method to localize not only Farsi/Arabic and Latin texts with different sizes, fonts and orientations but also low luminance contrast and poor quality ones in the natural images taken with uneven illumination conditions. Firstly, fast weighted median filtering as a nonlinear edge-preserving smoothing filter and then color contrast preserving decolorization are exploited to make the text localization system more robust for low luminance contrast and poor quality texts. In order to extract the Farsi/Arabic and Latin scene texts and also filter the nontext ones, a unified framework is proposed incorporating the maximally stable extremal regions and a novel proposed region detector called Stable Width Stroke Regions which is based on closed boundary regions. Phase congruency and Laplacian operators are exploited to extract the closed boundary regions. Finally, to extract the single text lines, the Meanshift clustering and radon transform were used. Experimental results show that the proposed method localize low luminance contrast and low quality scene texts for both Farsi/Arabic and Latin scripts encouragingly.

Visual object tracking using spatial Context Information and Global tracking skills

Tracking objects in videos by the mean shift algorithm with color weighted histograms has received much attention in recent years. However, the stability of weights in mean shift still needs to be improved especially under low-contrast scenes with complex motions. This paper presents a new type of color cue, which produces stable weights for mean shift tracking and can be computed pixel by pixel efficiently. The proposed color cue employs global tracking techniques to overcome the illustrated drawbacks of the mean shift algorithm. It represents a target candidate with a larger scale than that of the target model so that the model is much more precise than the candidate. We illustrate that the weights by this way are more reliable under various scenes. To further suppress surrounding clutters, we establish a new spatial context model so that the optimization results are a set of weights which can be computed pixel by pixel. The proposed color cue is called CIG since it computes the weights based on spatial Context Information and Global tracking skills. Experimental results on various tracking videos show that weight images by CIG have higher stability and precision than those of current methods especially under low-contrast scenes with complex motions.

A new detection operator for narrow band character extraction in low contrast scenes

Character detection plays an important role in character recognition systems. In this paper, we propose a new detection operator to extract the characters with approximately equivalent width from a low contrast simple natural scene. Initially, the Canny detector is applied to get the edge map. Afterwards, the distance transformation is performed on the edge map to get the regional maximum pixels (inner points). The distance information contributes to finding the outer contour (outer points) of the characters which compensates the gap of lacking edges. Then a new detection operator with a circular mask and a ring mask are used to act on the image along the inner and outer points to detect the characters. Our method belongs to the edge extraction category and local thresholding techniques but removes the dependency on edge accuracy. The experimental results of our tests validate the effectiveness and robustness of proposed method for various natural scenes.

A new Doppler ambiguity resolution technique for SAR data based on SPECAN processing

In this paper, we describe an approach for Doppler ambiguity resolution. This approach adopts the fact that unwrapped Doppler centroid is a linear function of range frequency for a given antenna squint angle. On this basis, an alternative azimuth compression method is addressed and performed in range frequency domain to accumulate target energy in the azimuth direction. The resulting trajectories behave as some straight lines with the average slope proportional to absolute Doppler centroid. This slope can be well estimated by image-contrast methods. Theoretical analysis and real measured SAR data show that the proposed approach works well in high- and relatively low-contrast scenes.

MODIFIED MULTILOOK CROSS CORRELATION (MLCC) ALGORITHM FOR DOPPLER CENTROID ESTIMATION IN SYNTHETIC APERTURE RADAR SIGNAL PROCESSING

The Multilook Cross Correlation (MLCC) is one of the most reliable algorithms used for Doppler ambiguity number estimation of the Doppler centroid parameter. However, the existing MLCC algorithm is only suitable for low contrast scenes. In high contrast scenes, the estimated result is not reliable, and the error is unacceptable. Besides, the Doppler centroid estimation processing time is long and can only be used in o†ine processing. In this paper, we introduce a modifled MLCC algorithm that has better sensitivity which is suitable not only for low contrast scenes, but also for high contrast scenes. In addition, the modifled MLCC algorithm can be implemented on parallel signal processing units for better time e-ciency. Experiments with RADARSAT-1 data show that the modifled algorithm works well in both high and low contrast scenes.

New Approach for SAR Doppler Ambiguity Resolution in Compressed Range Time and Scaled Azimuth Time Domain

A new approach is addressed for Doppler ambiguity resolution. This method exploits the fact that, in compressed range time and special scaled azimuth time domain, all the trajectories exhibit the same slope, which is proportional to the Doppler ambiguity number. The method can directly estimate the ambiguity number without a priori knowledge on the baseband Doppler centroid. It is confirmed that this approach is robust to the partially exposed strong targets in azimuth, ground slowly moving targets and the motion variation of platform in realistic scenarios. Results of real measured data processing show that this method works well in medium- to high-contrast scenes and can basically (not always) provide the right estimate in low-contrast scenes.

Coherent MapDrift Technique

A new parametric autofocus technique with a high accuracy of flight-parameter estimation dedicated to strip-mode synthetic aperture radar (SAR) systems is presented. Most of the known autofocus techniques require high-reflectivity targets (man-made targets) to obtain a properly focused SAR image. The technique proposed in this paper allows flight parameters to be estimated effectively, even for a low-contrast scene (e.g., forests, fields, small paths, etc.). The autofocus technique is based on well-known MapDrift (MD) principles. The presented technique is a coherent one, which allows flight parameters to be estimated more precisely than in the other well-known parametric technique referred to as classical MD. The presented technique allows flight parameters to be estimated with accuracy that is independent of the initial velocity error. It can be used for real-time processing for both Earth imaging and moving-target indication.

Active spectro-polarimetric imaging: signature modeling, imaging demonstrator and target detection

We report on the synthesis of our work on the analysis of active spectro-polarimetric imaging concept of real-world scenes. These synthesis extend from the investigation of depolarization mechanisms up to image analysis and processing. We show in particular that depolarization effects are wavelength dependent and strongly correlated with light absorption of materials. Multi-wavelength images of degree of polarization are recorded and analyzed, evidencing that the polarimetric image must be interpreted in conjunction with its counterpart intensity image in order to extract, the most relevant information from the scene. For real field operation, the noise characteristics of polarimetric images are also investigated under coherent laser illumination. The potential increase of target detection performance brought by properly processing the active polarimetric image is illustrated on a very low contrast scene.

PRF-ambiguity resolution for SAR by contrast minimization in range-Doppler domain

An algorithm was developed to accurately estimate the Doppler centroid, which is needed for high-quality synthetic aperture radar (SAR) image formation by resolving the SAR pulse repetition frequency (PRF) ambiguity. The algorithm uses the SAR range migration to resolve the PRF-ambiguity by searching for a PRF-ambiguity number that minimizes the intensity contrast in the range-Doppler domain. Experimental results show that the approach, compared with traditional methods for resolving the SAR PRF ambiguity, is more suitable for both high contrast scenes such as urban areas and low contrast scenes such as mountains. Moreover, the approach is more computationally efficient for there are no time-consuming correlations or fast Fourier transform (FFT) operations needed in the range-Doppler domain and only part of the range cells are used in the calculation.

Adding Sensitivity to the MLBF Doppler Centroid Estimator

The multilook beat frequency (MLBF) algorithm is the Doppler centroid estimator most commonly used in practice to solve the Doppler ambiguity. However, it still makes errors, notably in medium- or low-contrast scenes. In this paper, we present two ways in which the estimation sensitivity of the MLBF algorithm can be improved. First, we give a more thorough frequency-domain explanation of how the MLBF algorithm works and explain how cross beating and range migration cause estimation difficulties. The first improvement to the algorithm replaces the fast Fourier transform (FFT)-based beat frequency estimator with a more accurate one that uses phase increments. It avoids the FFT limitations of resolution and quantization, especially when the signal is discontinuous in one range cell due to range cell migration or burst mode operation (ScanSAR). A second improvement uses range cell migration correction to straighten the target trajectories before the beat frequency estimator is applied. This has the effect of narrowing the bandwidth of the beat signal and reducing the effect of cross beating. Finally, experiments with RADARSAT-1 data are used to illustrate the improved estimation accuracy of the modified algorithm

Visual Quality Determines the Direction of Neural Repetition Effects

One ubiquitous finding in functional magnetic resonance imaging studies is that repeated stimuli elicit lower responses than novel stimuli. In apparent contradiction, some studies have reported the exact opposite effect--greater responses to repeated than novel stimuli--in many of the same brain regions. Interestingly, these latter enhancement effects are typically obtained when stimuli have been degraded. To explore this observation, the present study examines the degree to which visual quality mediates repetition effects in a stimulus-selective ventral visual area. Subjects were presented with grayscale photographs of scenes that were either near or substantially above visual threshold, as determined by calibrating image contrast to behavioral performance. The presentation of 2 identical high-contrast scenes elicited lower blood oxygen level-dependent (BOLD) responses than the presentation of 2 different high-contrast scenes (repetition attenuation). Conversely, the presentation of 2 identical low-contrast scenes elicited greater BOLD responses than the presentation of 2 different low-contrast scenes (repetition enhancement). Neurophysiological studies suggest that repetition attenuation in ventral visual areas may reflect the reactivation of perceptual representations that have become sparse and selective as a result of prior experience, whereas repetition enhancement may reflect spared access to existing representations by severely degraded input.