  A moire pattern in the images is resulting from high frequency patterns
captured by the image sensor (colour filter array) that appear after
demosaicing. These Moire patterns would appear in natural images of scenes with
high frequency content. The Moire pattern can also vary intensely due to a
minimal change in the camera direction/positioning. Thus the Moire pattern
depreciates the quality of photographs. An important issue in demoireing
pattern is that the Moireing patterns have dynamic structure with varying
colors and forms. These challenges makes the demoireing more difficult than
many other image restoration tasks. Inspired by these challenges in demoireing,
a multilevel hyper vision net is proposed to remove the Moire pattern to
improve the quality of the images. As a key aspect, in this network we involved
residual channel attention block that can be used to extract and adaptively
fuse hierarchical features from all the layers efficiently. The proposed
algorithms has been tested with the NTIRE 2020 challenge dataset and thus
achieved 36.85 and 0.98 Peak to Signal Noise Ratio (PSNR) and Structural
Similarity (SSIM) Index respectively.
