Image Inpainting Method with Improved Patch Priority and Patch Selection

Rao, B. Janardhana; Chakrapani, Y.; Kumar, Sunil

doi:10.1080/09747338.2018.1474808

Cited by 9 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this test, four videos are considered. The state‐of‐the‐art algorithms of Giryes and Elad [40], Zarif et al [41], Newson et al [20], Janardhana Rao et al [21] and Yang Li et al [22] are used as the comparison baseline, as shown in Table 3. As mentioned in Table 3, three loss rates are considered, viz., 10, 25 and 45% for each video sequence.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Tudavekar

Patil

Saraf

2020

CAAI trans. intell. technol.

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

“…However, structural connectivity is not yet complete. Janardhana Rao et al [21] recognised that the priority of the patch falls to a low value due to the rapid decrease in confidence term at lower iterations. To overcome this effect, regularisation factor is added to the confidence term.…”

Section: Introductionmentioning

confidence: 99%