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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
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    This study introduces a novel closed-loop scheme for pan-sharpening, using an invertible neural network (INN) to simultaneously learn image enhancement and degradation. This approach regularizes solutions for improved multispectral (MS) image super-resolution.

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    Area of Science:

    • Remote Sensing
    • Computer Vision
    • Image Processing

    Background:

    • Pan-sharpening is an ill-posed problem in multispectral (MS) image super-resolution due to the difficulty in learning the nonlinear mapping from low-resolution (LR) to high-resolution (HR) MS images.
    • The vast solution space of possible pan-sharpening functions makes optimal mapping estimation challenging.

    Purpose of the Study:

    • To propose a closed-loop scheme that regularizes the solution space for pan-sharpening by simultaneously learning the forward (pan-sharpening) and backward (degradation) mapping.
    • To enhance the invertible neural network (INN) with a multiscale high-frequency texture extraction module for improved detail preservation.

    Main Methods:

    • Introduction of an invertible neural network (INN) to perform a bidirectional closed-loop for simultaneous pan-sharpening and degradation learning.
    • Development of a multiscale high-frequency texture extraction module to enhance the INN's ability to preserve fine details.

    Main Results:

    • The proposed closed-loop algorithm demonstrates superior performance compared to state-of-the-art methods in both qualitative and quantitative evaluations.
    • The method achieves favorable results with fewer parameters and verifies the effectiveness of the closed-loop mechanism through ablation studies.

    Conclusions:

    • The proposed closed-loop scheme effectively addresses the ill-posed nature of pan-sharpening by regularizing the solution space.
    • The integration of an INN and a high-frequency texture module offers a promising direction for advanced MS image super-resolution.