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Upsampling01:22

Upsampling

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...

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Related Experiment Video

Updated: Jul 11, 2026

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
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JPEG Image Enhancement with Pre-Processing of Color Reduction and Smoothing.

Akane Shoda1, Tomo Miyazaki1, Shinichiro Omachi1

  • 1Graduate School of Engineering, Tohoku University, Sendai 9808579, Japan.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary

This study introduces a novel deep learning method to enhance JPEG image quality by pre-processing images to reduce color and gradient complexity. This approach suppresses compression artifacts like block noise and pseudo-contours without needing high-performance decoding devices.

Keywords:
JPEGdeep learningimage compressionimage enhancementpre-processingsignal processing

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • JPEG is the standard for image compression due to its efficiency.
  • Deep learning methods improve JPEG quality but demand high-performance hardware for decoding.
  • Existing methods often require modifications to the JPEG decoding algorithm.

Purpose of the Study:

  • To propose a novel method for generating high-quality JPEG images using deep learning.
  • To avoid the need for high-performance devices during image decoding.
  • To enhance image quality without altering the standard JPEG decoding process.

Main Methods:

  • The proposed method involves pre-processing images before JPEG compression.
  • Key techniques include color reduction and smoothing of gradient regions.
  • It utilizes a deep learning-based color transformation network and a signal processing-based pseudo-contour suppression model.

Main Results:

  • The method effectively suppresses red block noise and pseudo-contours in compressed images.
  • It achieves high-quality image generation without requiring specialized decoding hardware.
  • Experimental results demonstrate superior performance compared to standard JPEG compression.

Conclusions:

  • The developed method offers a computationally efficient way to improve JPEG image quality.
  • It successfully mitigates common compression artifacts, enhancing visual perception.
  • This approach provides a practical solution for high-quality image compression using deep learning.