Abstract
Discretized image signals might have a lower dynamic range than the display. Because of this, false contours might appear when the image has the same pixel value for a larger region and the distance between pixel levels reaches the noticeable difference threshold. There have been several methods aimed at approximating the high bit depth of the original signal. Our method models a region with a bended plate model, which leads to the biharmonic equation. This method addresses several new aspects: the reconstruction of non-continuous regions when foreground objects split the area into separate regions; the incorporation of confidence about pixel levels, making the model tunable; and the method gives a physics-inspired way to handle local maximal/minimal regions. The solution of the biharmonic equation yields a smooth high-order signal approximation and handles the local maxima/minima problems.
Highlights
Digital images are quantized signals, and quantization introduces quantization error.De-quantization approximates the original signal based on the digital samples
Partial differential equation-based signal de-quantization is a very appealing approach, but we identified the following areas where the previous approaches could be improved:
The advantages of the higher-order approximation are obvious in the figure: the maximal region is smoothly recovered, and there is no second-order false contour
Summary
Digital images are quantized signals, and quantization introduces quantization error. De-quantization approximates the original signal based on the digital samples. The intensities in the pixels of the digital images are represented with integer values in the range of [0, 2n − 1], where n is called the bit depth. Low bit depth images have fewer levels than the display which is used for presentation. De-quantization aims to decrease artifacts originating from the quantization process. Bit-depth extension or enhancement are used to describe de-quantization
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