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Related Concept Videos

Upsampling01:22

Upsampling

265
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
265
Downsampling01:20

Downsampling

188
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.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Scaling01:26

Scaling

278
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
278
Basic Operations on Signals01:22

Basic Operations on Signals

419
Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
419
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

241
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
241
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

370
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
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Updated: Jul 22, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

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Published on: July 5, 2024

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Scale-Arbitrary Invertible Image Downscaling.

Jinbo Xing, Wenbo Hu, Menghan Xia

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    We developed a Scale-Arbitrary Invertible Image Downscaling Network (AIDN) to enable high-resolution image downscaling for social media. This network allows for arbitrary scale factors, preserving details for later restoration from low-resolution images.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Social media platforms often downscale high-resolution (HR) images, losing visual details.
    • Existing invertible image downscaling methods are limited to fixed integer scale factors.

    Purpose of the Study:

    • To propose an effective and universal Scale-Arbitrary Invertible Image Downscaling Network (AIDN).
    • To enable invertible image downscaling with arbitrary scale factors, suitable for social media resolution restrictions.

    Main Methods:

    • Developed the Scale-Arbitrary Invertible Image Downscaling Network (AIDN).
    • Introduced a Conditional Resampling Module (CRM) to handle arbitrary scale factors by conditioning on scale and image content.
    • Embedded HR information imperceptibly into downscaled LR images for restoration.

    Main Results:

    • AIDN achieves top performance for invertible downscaling with arbitrary integer and non-integer scale factors.
    • The network demonstrates robustness against lossy image compression.
    • HR images can be restored solely from the downscaled LR images.

    Conclusions:

    • AIDN offers a universal solution for invertible image downscaling with arbitrary scales.
    • The method effectively preserves visual details for social media applications.
    • AIDN maintains performance even after image compression, enhancing practical utility.