78 4. A Variational approach for the <strong>de</strong>striping issue Figure 4.2 – (Left) Noisy image from Terra MODIS band 30 (Right) Denoising with TV regularization (ID=0.51) which can take the simple form : −2λ(u − f) + div ( ) ∇u = 0 (4.22) |∇u| From a computational point of view, the non-differentiability of the term |∇u| for ∇u =0 is problematic. In [Acar and Vogel, 1994], the authors suggest a smoothed version of the energy functional where the total variation norm is relaxed with a small positive parameter ɛ. The original ROF energy functional (4.20) becomes : ∫ √ E ɛ (u) =λ‖u − f‖ 2 + |∇u| 2 + ɛdΩ (4.23) which leads to the following Euler-Lagrange Equation : ( ) ∇u −2λ(u − f) + div √ = 0 (4.24) |∇u| 2 + ɛ 2 From the following inequality : ∫ ∫ ∀u ∈ L 1 (Ω), |∇u|dΩ ≤ Ω Ω Ω √ ∫ |∇u| 2 + ɛdΩ ≤ Ω |∇u|dΩ+ √ ɛ|Ω| (4.25) it follows that : √ ∫ lim |∇u| ɛ→0 ∫Ω 2 + ɛdΩ = |∇u|dΩ (4.26) Ω When the value of ɛ tends to zero, the solution of the optimisation problem (4.20) converges to that of (4.25).
79 Fast and exact minimization algorithms for TV-based energy functionals have been subject to intense research, and many methods can be used to solve ROF mo<strong>de</strong>l. In their original work, Rudin, Osher and Fatemi relied on a fixed step gradient <strong>de</strong>scent. Quasi-Newton schemes have been investigated in [Chambolle and Lions, 1997], [Dobson and Vogel, 1997], and [Nikolova and Chan, 2007]. The dual formulation of TV minimization was explored in [Cha] and later lead to Antonin Chambolle’s projection algorithm [Chambolle, 2004]. The approach <strong>de</strong>veloped by Chambolle was the first to provi<strong>de</strong> a solution to the exact TV minimization problem (4.20) instead of a smoothed version. More recently, techniques based on graph cuts have proved successfull in [Boykov et al., 2001] and [Dar]. In this section, we propose a fixed point technique based on a Gauss-Sei<strong>de</strong>l iterative method. The image domain Ω is discretized into a grid where (x i = ih) and (y i = jh), h being the cell size. Let us <strong>de</strong>note D + , D − and D 0 the forward, backward and central finite differences. In the x-direction for exemple, we can write : (D ±x u) i,j = ± u i±1,j − u i,j h (D 0x u) i,j = u i+1,j − u i−1,j 2h (4.27) The divergence operator is implemented using the non-negative discretization which offers more stability than a discretization based solely on central differences. The discret form of Euler-Lagrange equation (4.24) is : u i,j = f i,j + 1 2λ D −x + 1 2λ D −y ( ( ) D +x u i,j √ (D+x u i,j ) 2 +(D 0y u i,j ) 2 + ɛ 2 ) D +y u i,j √ (D0x u i,j ) 2 +(D +y u i,j ) 2 + ɛ 2 ( ) = f i,j + 1 u i+1,j − u i,j 2λh 2 √ (D+x u i,j ) 2 +(D 0y u i,j ) 2 + ɛ − u i,j − u i−1,j √ 2 (D−x u i,j ) 2 +(D 0y u i−1,j ) 2 + ɛ 2 ( ) + 1 u i,j+1 − u i,j 2λh 2 √ (D0x u i,j ) 2 +(D +y u i,j ) 2 + ɛ − u i,j − u i,j−1 √ 2 (D0x u i,j−1 ) 2 +(D −y u i,j ) 2 + ɛ 2 (4.28) The linearization of the previous equation leads to : ⎛ ⎞ u n+1 i,j = f i,j + 1 u ⎝ n i+1,j − un+1 i,j u n+1 i,j − u n i−1,j 2λh 2 − √ ⎠ √(D +x u n i,j )2 +(D 0y u n i,j )2 + ɛ 2 (D −x u n i,j )2 +(D 0y u n i−1,j )2 + ɛ 2 ⎛ ⎞ + 1 u ⎝ n i,j+1 − un+1 i,j u n+1 i,j − u n i,j−1 2λh 2 − √ ⎠ √(D 0x u n i,j )2 +(D +y u n i,j )2 + ɛ 2 (D 0x u n i,j−1 )2 +(D −y u n i,j )2 + ɛ 2 (4.29)