A behavioural method for assessing the performance of image denoising algorithms

D.H. Baker, R.J. Summers, T.S. Meese

Research output: Contribution to journalConference abstract

Abstract

Numerous algorithms exist for removing unwanted noise from digital images. Typically, these are inspired by biological visual systems and involve bandpass filtering and thresholding to remove the noise. However, the efficacy of such algorithms is usually assessed numerically (eg by calculating the root mean squared error between original and denoised images) with little regard to perceptual consequences for the end user. This can—in principle—lead to situations where two algorithms are equally ‘good’ numerically, yet one may produce highly salient artefacts whilst the other does not. We propose a novel behavioural method for comparing denoised images. Using segments of 308 images from a public image library, we measured contrast thresholds for three observers detecting Gaussian pixel noise added to the image in one interval of a 2IFC experiment; these constitute our baseline. We then repeated the experiment, having passed all of the stimuli (images with or without noise added) through a denoising algorithm (Fischer et al, 2007 International Journal of Computer Vision 75 231–246). The increase in threshold relative to the baseline (typically a factor of ~2) provides an index of the success of the denoising algorithm by giving an indication of the amount of perceptually meaningful (eg visible) noise that is ‘hidden’ by the algorithm. We use this technique to compare polar-separable and cartesian-separable log-Gabor filters, as well as filters of different orientation bandwidths. Thresholds occurred at a constant peak-signal-to-noise ratio for baseline and denoised conditions, linking numerical comparisons with a measure of perceptual validity for the end user.
Original languageEnglish
Article number13
Pages (from-to)1271
Number of pages1
JournalPerception
Volume41
Issue number10
DOIs
Publication statusPublished - Oct 2012
EventBiological and Machine Vision 2: Joint meeting of the Applied Vision Association and the British Machine Vision Association - Cambridge, United Kingdom
Duration: 21 May 2012 → …

Bibliographical note

Abstracts.

Fingerprint Dive into the research topics of 'A behavioural method for assessing the performance of image denoising algorithms'. Together they form a unique fingerprint.

  • Research Output

    • 1 Conference abstract

    Linking image compression and image analysis through Taylor–Riesz pyramids

    Langley, K. & Anderson, S. J., Oct 2012, In : Perception. 41, 10, p. 1267 1 p., 1.

    Research output: Contribution to journalConference abstract

    Open Access
  • Cite this