JOINT CONTENT-ADAPTIVE DICTIONARY LEARNING AND SPARSE SELECTIVE EXTRAPOLATION FOR CROSS-SPECTRAL IMAGE RECONSTRUCTION

Numerous applications deal with distorted images, e.g., during transmission over lossy channels in image coding, or to reconstruct occlusions in multi-color multi-view imaging scenarios. In many cases, not all spectral channels are distorted, or the losses distribute differently between the channels. Recently, efforts were made to develop reference guided approaches to reconstruct distorted spectral content. However, these methods are only able to exploit information from a single reference, even if there are multiple spectral components available that could be used for reconstruction. To overcome this limitation, a novel method is proposed in this paper, which introduces a content-adaptive dictionary learning approach that is applicable to an arbitrary number of references. With the novel approach, an average PSNR gain of approx. 1.5 dB is achieved in comparison to the best recently published state-of-the-art methods for a single reference component. Moreover, the presence of multiple guidance channels can enhance the reconstruction by another 6 dB on average. At the same time, the complexity of the novel algorithm is significantly lower than for previously published methods.