Underlying many techniques of image restortation, quantization, and enhancement is the mathematically convenient, but visually unsuitable distortion measure of squared difference in intensity. Squared-intensity difference has an indirect phenomenological correspondence in a model of the visual system. We have undertaken, therefore, an experiment that derives a new distortion measure from an acceptable visual system model and compares it in a fair test against squared difference in intensity in an image restoration task. We start with an eye–brain system model, inferred from the works of current vision researchers, which consists of a bank of parallel spatial frequency channels and image detectors. From this model we derive a new distortion criterion that is related to changes in the per-channel detection probability and phase angle. The optimal linear (Wiener) filters for each distortion measure operate in turn on the same noisy incoherent images. The results show that the filter for the new distortion measure yields a superior restoration. It is more visually agreeable, more sharply detailed, and truer in contrast compared to the squared-difference filter, and impressive in its own right. Its mathematical properties suggest that significantly increased efficiency in the storage or communication of images may be gained by its use.
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