Automatic movie restoration based on wave atom transform and nonparametric model

Abstract

This article presents a new scheme for movie de-scratching, de-noising and de-blotching based on wave atom transform and nonparametric model. According to the analysis of the noise, scratches and blotches in the film, we establish a hybrid model for movie signal. In the proposed model, we model scratch as directional additive noise, which can be represented effectively by a new multi-scale analysis tool called wave atom transform. Based on wave atoms, de-noising and de-scratching are combined in a nested thresholding framework which can suppress both homogeneous noise and structural artifacts. On the other hand, blotch is described as multiplicative noise in the model, which can be restored by a "detecting and inpainting" strategy. We use the inter-and intra-frame discontinuity of blotch to detect them and then restore them by kernel regression method. Combining the processing operations above together, we design an effective automatic movie restoration system. Experimental results show that the proposed method not only achieves excellent performances even for seriously damaged movies but also has low computational complexity compared with the existing methods.

Fractal Dimension Invariant Filtering and Its CNN-Based Implementation

“…To our surprise, although in the following section we show that fractal-based image model is very suitable for the problem of curve detection, very few existing methods apply fractal analysis to solve the problem. Taking advantage of the directionality of curve, early curve detectors are based on diverse transformations, including the Hough transformation [9], the curvelets [35], the wave atoms [43]. Besides the direction, the multiscale property of curve is considered via applying multiscale Fourier transformation [6], Frangi filtering [11], and the scale-space distance transformation [34].…”