Dual‐tree complex wavelet transform and super‐resolution based video inpainting application to object removal and error concealment. Issue 4 (12th November 2020)

• Record Type: Journal Article
• Authors: Tudavekar, Gajanan; Patil, Sanjay R.; Saraf, Santosh S.
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 4(2020)
• Page Start: 314
• Page End: 319
• Publication Date: 2020-11-12
• Subjects: image sequences -- trees (mathematics) -- fuzzy logic -- image denoising -- video signal processing -- image enhancement -- image resolution -- image restoration -- wavelet transforms -- matrix algebra; video sequences -- object removal -- error concealment -- dual‐tree complex -- super‐resolution based video -- target region -- two‐stage framework -- image resolution -- criminisi algorithm -- auto‐regression technique -- fuzzy logic‐based histogram equalisation -- image brightness -- super‐resolution technique -- down‐sampling -- video inpainting algorithms -- structural similarity index matrix -- visual information; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0045
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.