Dual-tree complex wavelet transform and super-resolution based video inpainting application to object removal and error concealment

Video inpainting is a technique that fills in the missing regions or gaps in a video by using its known pixels. The existing video inpainting algorithms are computationally expensive and introduce seam in the target region that arises due to variation in brightness or contrast of the patches. To overcome these drawbacks, the authors propose a novel two-stage framework. In the first step, sub-bands of wavelets of a low-resolution image are obtained using the dual-tree complex wavelet transform. Criminisi algorithm and auto-regression technique are then applied to these sub-bands to inpaint the missing regions. The fuzzy logic-based histogram equalisation is used to further enhance the image by preserving the image brightness and improve the local contrast. In the second step, the image is enhanced using super-resolution technique. The process of down-sampling, inpainting and subsequently enhancing the video using the super-resolution technique reduces the video inpainting time. The framework is tested on video sequences by comparing and analysing the structural similarity index matrix, peak-signal-to-noise ratio, visual information fidelity in pixel domain and execution time with the state-of-the-art algorithms. The experimental analysis gives visually pleasing results for object removal and error concealment.