Maximum a Posterior and Perceptually Motivated Reconstruction Algorithm: A Generic Framework
Most of the existing image reconstruction algorithms are application specific, and have generalization issues due to the need for parameter tuning and an unknown level of signal distortion. Addressing these problems, in this paper, we propose an efficient perceptually motivated and maximum a posterior (MAP)-based generic framework for image reconstruction. This can be applied to several image/video processing applications, where there is a necessity to improve reconstruction accuracy and suppress visible artifacts, such as denoising, deinterlacing, interpolation, de-blocking of Jpeg/Jpeg-2000, and demosaicing. The gradient magnitudes are noise insensitive to a moderate levels of noise and we propose to utilize this property for finding pixels with similar edge semantics in the neighborhood when neighboring pixels are noisy. With this view, we incorporate the gradient magnitude similarity based image quality assessment metric with the MAP estimation and, in turn, it can better approximate the variance of the MAP, as compared to nonlinear filters. The proposed generic algorithm (without manually tuning any parameters) is shown to produce a better quality of reconstruction when compared to the state-of-the-art application-specific algorithms, for most of the image processing applications.