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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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PIABC: Point Spread Function Interpolative Aberration Correction.

Chanhyeong Cho1, Chanyoung Kim1, Sanghoon Sull1

  • 1School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-Gu, Seoul 02841, Republic of Korea.

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Summary

This study introduces a novel framework using densely interpolated Point Spread Functions (PSFs) to enhance image quality in digital cameras. The method effectively reduces sensor noise and optical distortions for clearer, high-fidelity images.

Keywords:
CMOS image sensorWiener filterautoencoderchromatic-spatial simulated PSFdeep residual refinementoptical aberration correctionpixel-wise PSF interpolationpost-sensor image restorationsensor noise suppressiontransformer

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Area of Science:

  • Digital Imaging
  • Computational Photography
  • Image Processing

Background:

  • High-resolution digital camera image quality suffers from Complementary Metal-Oxide-Semiconductor (CMOS) sensor noise and optical aberrations.
  • Sensor noise, especially at high International Organization for Standardization (ISO) settings, and optical imperfections like blur and chromatic fringing degrade image fidelity.
  • Separating optical and sensor noise is challenging, yet optical improvements may mitigate sensor noise.

Purpose of the Study:

  • To introduce a novel image restoration framework utilizing densely interpolated Point Spread Functions (PSFs) for high-fidelity image recovery.
  • To address image degradation caused by sensor noise and optical imperfections in digital imaging systems.
  • To demonstrate the benefit of incorporating explicit physical priors into image restoration processes.

Main Methods:

  • Simulated Gaussian-based PSFs representing pixel-wise chromatic and spatial distortions from degraded images.
  • Encoding PSFs into a latent space for feature enhancement and generating refined PSFs via similarity-weighted interpolation.
  • Applying interpolated PSFs through Wiener filtering, followed by residual correction for image restoration.

Main Results:

  • The proposed method significantly enhances structural restoration in images.
  • The framework effectively suppresses sensor-induced artifacts, improving perceptual quality.
  • Evaluations on DIV2K and RealSR-V3 datasets confirm superior performance compared to post-processing networks.

Conclusions:

  • Explicit physical priors, specifically densely interpolated PSFs, are beneficial for achieving high perceptual fidelity in image restoration.
  • The developed framework offers a robust pre-processing strategy for improving image quality in digital SLR systems.
  • This approach demonstrates a promising direction for mitigating combined optical and sensor noise artifacts.