Mitigating risks and maximizing sustainability of treated wastewater reuse for irrigation.

Scarcity of freshwater for agriculture has led to increased utilization of treated wastewater (TWW), establishing it as a significant and reliable source of irrigation water. However, years of research indicate that if not managed adequately, TWW may deleteriously affect soil functioning and plant productivity, and pose a hazard to human and environmental health. This review leverages the experience of researchers, stakeholders, and policymakers from Israel, the United-States, and Europe to present a holistic, multidisciplinary perspective on maximizing the benefits from municipal TWW use for irrigation. We specifically draw on the extensive knowledge gained in Israel, a world leader in agricultural TWW implementation. The first two sections of the work set the foundation for understanding current challenges involved with the use of TWW, detailing known and emerging agronomic and environmental issues (such as salinity and phytotoxicity) and public health risks (such as contaminants of emerging concern and pathogens). The work then presents solutions to address these challenges, including technological and agronomic management-based solutions as well as source control policies. The concluding section presents suggestions for the path forward, emphasizing the importance of improving links between research and policy, and better outreach to the public and agricultural practitioners. We use this platform as a call for action, to form a global harmonized data system that will centralize scientific findings on agronomic, environmental and public health effects of TWW irrigation. Insights from such global collaboration will help to mitigate risks, and facilitate more sustainable use of TWW for food production in the future.

Rapid super resolution for infrared imagery.

Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.

Joint estimation of unknown radiometric data, gain, and offset from thermal images.

Low cost, weight, and size microbolometer-based thermal focal plane arrays are attractive for thermal-imaging applications. Under environmental loads like those in agricultural remote sensing, these cameras tend to suffer from drift in gain and offset with time and thus require constant calibration. Our goal is to skip this step via computational imaging. In a previous work we estimated the unknown offset value and radiometric image of an object, given the calibrated gain, from a pair of successive images taken at two different blur levels, eliminating the need for offset calibration due to temperature variation. Here, we extend our model to a case with unknown gain and offset. We show that these values, as well as the objects' radiometric value, can be found jointly by minimizing a cost function relying on N pairs of blurred and sharp images. The method addresses both space-invariant and space-variant cases. Simulations show promising accuracy with error characterized by root mean squared error of less than 1.6°C.

Radiometric imaging by double exposure and gain calibration.

Due to low cost and small size, uncooled microbolometer-based thermal focal plane arrays are very attractive for radiometry. However, being non-cooled, they suffer from temporally and spatially dependent changes that require constant calibration. While the gain calibration can be reasonably realized by two-point correction, the offset due to internal radiation loads poses a complicated calibration scheme. We present a new computational optics approach that simplifies the essential calibration for temperature offset. Using two successive images of the object taken with different known blur levels, one can eliminate the object term from the image-formation equation, resulting in an equation for the unknown sensor offset. A general algebraic model is presented for the space-variant case followed by solutions using both direct inverse method and iterative solver. The new scheme allows restoration of the radiometric value within 1% error with the direct method, and 0.2% error with the iterative scheme. Account of the influence of realistic lens positioning error on restoration accuracy was given. Results using direct inverse methods for restoring the radiometric values yield restoration error with a good average error of 3.7% and less.

Optimized depth of field methodology using annular liquid crystal modulator assisted by image processing.

An optical-digital tunable depth of field (DOF) methodology is presented. The suggested methodology forms a fused image based on the sharpest similar depth regions from a set of source images taken with different phase masks. Each phase mask contains a different degree of DOF extension and is implemented by using an annular liquid crystal spatial light modulator, which consists of 16-ring electrodes positioned in the pupil plane. A detailed description of the optical setup and characterization of selected pupil phase masks as well as optimization of the binary phase mask for maximal DOF extension is presented. Experimental results are investigated both qualitatively and quantitatively. In addition, the algorithm's results are compared with those of some well-known fusion algorithms and proved its supremacy.

Blurred and noisy image pairs in parallel optics.

In previous works we have shown that parallel optics (PO) architecture can be used to improve the system matrix condition, which results in improving its immunity to additive noise in the image restoration process. PO is composed of a "main" system and an "auxiliary" system. Previously, we suggested the "trajectories" method to realize PO. In that method, a required auxiliary system is composed from auxiliary optics with a pixel confined response, followed by signal processing. In this paper, we emphasize the important secondary effects of the trajectories method. We show that in such a system, where the postprocessing comes after the detection, the postprocessing acts as a noise filter, hence allowing us to work with noisy data in the auxiliary channel. Roughly speaking, the SNR of an imaging system depends on the numerical aperture (NA). It follows that the main system, which typically has a higher NA, also has a higher SNR. Hence in the PO system, the ratio between the NA values of the main and auxiliary systems is expected to dictate the gap between their SNR values. In this paper, we show that when the system is implemented by the trajectories method, this expectation is too conservative. It is shown that due to the noise filtering, the auxiliary system can be noisier than expected. This claim is proved analytically and verified and exemplified by using experimental measurements.

Extended depth of field using a liquid crystal annular spatial light modulator.

A detailed investigation is presented on the tunable extended depth of field (EDOF) method, proposed recently by Klapp et al. [Opt. Lett.39, 1414 (2014)]. This method is based on temporal multiplexing of phase masks, using an annular liquid crystal spatial light modulator possessing a small number of rings. Examples of 3D simulations used to determine the phase profiles in the pupil plane are presented, as well as more detailed experimental results. Both the experimental and numerical results include comprehensive analysis of contrast dependence on both the spatial spectrum of the object and the amount of defocus. In addition, for the first time, we present the EDOF order inversion in the experimental and simulated data. The results show a profound performance of the proposed system and method.

Tunable extended depth of field using a liquid crystal annular spatial filter.

A tunable extended depth of field (EDOF) imaging is presented using temporal multiplexing and a low-cost eight-ring, annular liquid crystal spatial light modulator. By changing between different phase profiles in the pupil plane of a lens we perform several levels of EDOF. Using these levels as a "database" it is shown by temporal multiplexing how to decompose tunable levels of EDOF.

Design of an image restoration algorithm for the TOMBO imaging system.

The TOMBO system (thin observation module by bound optics) is a multichannel subimaging system over a single electronic imaging device. Each subsystem provides a low-resolution (LR) image from a unique lateral point of view. By estimating the image's lateral position, a high-resolution (HR) image is restored from the series of the LR images. This paper proposes an multistage algorithm comprised of successive stages, improving difficulties in previous suggested schemes. First, the registration algorithm estimates the subchannel shift parameters and eliminates bias. Second, we introduce a fast image fusion, overcoming visual blockiness artifacts that characterized previously suggested schemes. The algorithm fuses the set of sampled subchannel images into a single image, providing the reconstruction initial estimate. Third, an edge-sensitive quadratic upper bound term to the total variation regulator is suggested. The complete algorithm allows the reconstruction of a clean, HR image, in linear computation time, by the use of the linear conjugate gradient optimization. Finally, we present a simulated comparison between the proposed method and a previously suggested image restoration method. The results show that the proposed method yields better reconstruction fidelity while eliminating spatial speckle artifacts associated with the previously suggested method.

Experimental results for improving the matrix condition using a hybrid optical system.

We present preliminary experimental results for implementing the "blurred trajectories" method on three parallel optics (PO) systems. The "main" system and "auxiliary" optics were simple laboratory graded lenses attached to an iris diaphragm. When applying the blurred trajectories method we first show an improvement in the matrix condition, as the matrix condition number decreased in a range of factors of 3 to 418 relative to the main system. Following that, image restoration by weak regularization was performed so that the system matrix condition dominated the restoration process. It was shown that the restoration results of the PO are better than those of the main system and the auxiliary optics separately. In addition, the quality of the restoration follows the system's matrix condition. The improvement in the matrix condition achieved by the PO system improved the immunity to detection noise. Finally, a comparison to Wiener filtering restoration shows that it is also generally inferior to the proposed method.

Trajectories in parallel optics.

In our previous work we showed the ability to improve the optical system's matrix condition by optical design, thereby improving its robustness to noise. It was shown that by using singular value decomposition, a target point-spread function (PSF) matrix can be defined for an auxiliary optical system, which works parallel to the original system to achieve such an improvement. In this paper, after briefly introducing the all optics implementation of the auxiliary system, we show a method to decompose the target PSF matrix. This is done through a series of shifted responses of auxiliary optics (named trajectories), where a complicated hardware filter is replaced by postprocessing. This process manipulates the pixel confined PSF response of simple auxiliary optics, which in turn creates an auxiliary system with the required PSF matrix. This method is simulated on two space variant systems and reduces their system condition number from 18,598 to 197 and from 87,640 to 5.75, respectively. We perform a study of the latter result and show significant improvement in image restoration performance, in comparison to a system without auxiliary optics and to other previously suggested hybrid solutions. Image restoration results show that in a range of low signal-to-noise ratio values, the trajectories method gives a significant advantage over alternative approaches. A third space invariant study case is explored only briefly, and we present a significant improvement in the matrix condition number from 1.9160e+013 to 34,526.

Improvement of matrix condition of Hybrid, space variant optics by the means of parallel optics design.

The problem of image restoration of space variant blur is common and important. One of the most useful descriptions of this problem is in its algebraic form I=H*O, where O is the object represented as a column vector, I is the blur image represented as a column vector and H is the PSF matrix that represents the optical system. When inverting the problem to restore the geometric object from the blurred image and the known system matrix, restoration is limited in speed and quality by the system condition. Current optical design methods focus on image quality, therefore if additional image processing is needed the matrix condition is taken "as is". In this paper we would like to present a new optical approach which aims to improve the system condition by proper optical design. In this new method we use Singular Value Decomposition (SVD) to define the weak parts of the matrix condition. We design a second optical system based on those weak SVD parts and then we add the second system parallel to the first one. The original and second systems together work as an improved parallel optics system. Following that, we present a method for designing such a "parallel filter" for systems with a spread SVD pattern. Finally we present a study case in which by using our new method we improve a space variant image system with an initial condition number of 8.76e4, down to a condition number of 2.29e3. We use matrix inversion to simulate image restoration. Results show that the new parallel optics immunity to Additive White Gaussian Noise (AWGN) is much better then that of the original simple lens. Comparing the original and the parallel optics systems, the parallel optics system crosses the MSEIF=0 [db] limit in SNR value which is more than 50db lower then the SNR value in the case of the original simple lens. The new parallel optics system performance is also compared to another method based on the MTF approach.

Angular motion point spread function model considering aberrations and defocus effects.

When motion blur is considered, the optics point spread function (PSF) is conventionally assumed to be fixed, and therefore cascading of the motion optical transfer function (OTF) with the optics OTF is allowed. However, in angular motion conditions, the image is distorted by space-variant effects of wavefront aberrations, defocus, and motion blur. The proposed model considers these effects and formulates a combined space-variant PSF obtained from the angle-dependent optics PSF and the motion PSF that acts as a weighting function. Results of comparison of the new angular-motion-dependent PSF and the traditional PSF show significant differences. To simplify the proposed model, an efficient approximation is suggested and evaluated.