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Downsampling01:20

Downsampling

762
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...
762
Deconvolution01:20

Deconvolution

671
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
671
Upsampling01:22

Upsampling

690
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...
690

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Related Experiment Video

Updated: Mar 20, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
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Postprocessing of Compressed Images via Sequential Denoising.

Yehuda Dar, Alfred M Bruckstein, Michael Elad

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 24, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to reduce image compression artifacts by treating it as an inverse problem. The technique significantly enhances image quality for JPEG, JPEG2000, and HEVC compression.

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    Area of Science:

    • Digital Image Processing
    • Computer Vision
    • Signal Processing

    Background:

    • Image compression techniques like JPEG, JPEG2000, and HEVC introduce visual artifacts.
    • Reducing these artifacts is crucial for maintaining image fidelity in various applications.

    Purpose of the Study:

    • To propose a novel postprocessing technique for effective compression-artifact reduction.
    • To leverage state-of-the-art image denoising algorithms within an inverse problem framework.

    Main Methods:

    • The approach formulates artifact reduction as an inverse problem with regularization.
    • It utilizes the Plug-and-Play Prior framework and the alternating direction method of multipliers (ADMM).
    • A key step involves linearizing the compression-decompression process for optimization.

    Main Results:

    • The method demonstrates impressive gains in image quality across multiple compression standards.
    • The technique is shown to be effective for transform coding-based compression methods.
    • A thorough analysis of the linear approximation for basic compression procedures is provided.

    Conclusions:

    • The proposed postprocessing technique offers a robust solution for compression-artifact reduction.
    • The method integrates advanced denoising algorithms to improve image quality.
    • This work advances the field of image restoration for compressed images.