The arrangement of plant roots and their overall structure, known as root system architecture (RSA), plays an important role in acquiring water and nutrients essential for plant growth and development. Moreover, the RSA demonstrates remarkable adaptability to environmental stresses, making it a central factor in plant adaptation. Root traits, including root length, root diameter, root length density (RLD), and the presence of root hairs, play a crucial role in optimizing resource utilization within the soil and enhancing productivity. In particular, root hairs play a crucial role in the overall health and functioning of plants. These microscopic, hair-like structures extend from the surface of root cells and greatly increase the root’s surface area, which accounts for approximately 70% of the total root area. The characteristics of root hairs, such as their length and density, significantly enhance soil nutrients and water uptake. Considering these advantages, it is difficult to observe root hairs in a scene with low resolution. Therefore, we proposed a study using deep learning-based image super-resolution methods as a pre-processing step that helps to reconstruct finer details and structures within the root hairs, leading to a more accurate representation of their morphology, to understand the improvement in the response of root hairs under different environmental conditions and their impact on nutrient and water uptake, models need to be evolved.
This paper proposes a novel steganographic method that employs a feedback mechanism to improve the efficiency and stealth of data hiding within the Discrete Cosine Transform (DCT) coefficients of JPEG images. This method enhances the correlation between the hidden message and the cover image, while minimizing the perceptible changes to the image. The system starts by dividing the cover image into blocks and applying DCT to each. It then evaluates the correlation between the hidden message and the DCT coefficients to identify potential data embedding points. A trained decision rules algorithm then chooses the optimal data embedding technique, considering factors like the size and location of the DCT coefficient within image blocks. Different embedding techniques are employed. The system subsequently generates feedback based on metrics such as image quality and data detectability, refining the decision ruls's effectiveness over time. By employing this dynamic approach, our system adaptively improves the data hiding process, enhancing capacity and minimizing detectability. This work opens new doors in the realm of steganography, presenting an intelligent system capable of adaptively embedding data with optimized stealth and efficiency.
The BGU CubeSat satellite is from a class of low-cost, compact satellites. Its dimensions are 10×10×30 cm. It is equipped with a low resolution 256×320 pixels short wave infrared (SWIR) camera at the 1.55-1.7mm wavelength band. Images are transmitted in bursts of tens of images at a time with few pixel shifts from the first image to the last. Each image burst is suitable for Multiple Image Super Resolution (MISR) enhancements. MISR can construct a high-resolution (HR) image from several low-resolution (LR) images yielding an image that can resolve more details that are crucial for research in remote sensing. In this research, we verify the applicability of SOTA deep learning MISR models that were developed following the publication of the PROBA-V MISR satellite dataset at the visible red and near IR. Our SWIR multiple images differ from PROBA-V by the spectral band and by the method of collecting multiple images of the exact location. Our imagery data is acquired by a burst of very close temporal images. PROBA-V revisits the satellite at a period smaller than 30 days, assuming the soil dryness is about the same. We compare the results of Single Image Super-Resolution (SISR) and MISR techniques to "off-the-shelf" products. The quality of the super-resolved images is compared by nonreference metrics suitable for remote sensing applications and by experts' visual inspection. Unlike remarkable achievements by the GAN technique that can achieve very appealing results that are not always faithful to the original ground truth, the super-resolved images should preserve the original details as much as possible for further scientific remote sensing analysis.
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