The Frobenius FFT

Joris van der Hoevena, Robin Larrieub

CNRS, Laboratoire d'informatique

École polytechnique

91128 Palaiseau Cedex


a .


b .


April 30, 2017


Let be the finite field with elements and let be a primitive -th root of unity in an extension field of . Given a polynomial of degree less than , we will show that its discrete Fourier transform can be computed essentially times faster than the discrete Fourier transform of a polynomial of degree less than , in many cases. This result is achieved by exploiting the symmetries provided by the Frobenius automorphism of over .


FFT; finite field; complexity bound; Frobenius automorphism

A.C.M. subject classification: G.1.0 Computer-arithmetic, F.2.1 Number-theoretic computations

A.M.S. subject classification: 68W30, 68Q17, 68W40


Let and let be a primitive -th root of unity in . The traditional algorithm for computing discrete Fourier transforms [7] takes a polynomial of degree on input and returns the vector . If actually admits real coefficients, then we have for all , so roughly of the output values are superfluous. Because of this symmetry, it is well known that such “real FFTs” can be computed roughly twice as fast as their complex counterparts [3, 25].

More generally, there exists an abundant literature on the computation of FFTs of polynomials with various types of symmetry. Crystallographic FFT algorithms date back to [26], with contributions as recent as [20], but are dedicated to crystallographic symmetries. So called lattice FFTs have been studied in a series of papers initiated by [11] and continued in [27, 4]. A more general framework due to [1] was recently revisited from the point of view of high-performance code generation [18]. Further symmetric FFT algorithms and their truncated versions were developed in [16].

In this paper, we focus on discrete Fourier transforms of polynomials with coefficients in the finite field with elements. In general, primitive -th roots of unity only exist in extension fields of , say . This puts us in a similar situation as in the case of real FFTs: our primitive root of unity lies in an extension field of the coefficient field of the polynomial. This time, the degree of the extension is instead of . As in the case of real FFTs, it is natural to search for algorithms that allow us to compute the DFT of a polynomial in approximately times faster than the DFT of a polynomial in .

The main purpose of this paper is to show that such a speed-up by an approximate factor of can indeed be achieved. The idea is to use the Frobenius automorphism as the analogue of complex conjugation. If , then we will exploit the fact that for all and . This means that it suffices to evaluate at only one element of each orbit for the action of the Frobenius automorphism on the cyclic group generated by . We will give an efficient algorithm for doing this, called the Frobenius FFT.

Our main motivation behind the Frobenius FFT is the development of faster practical algorithms for polynomial multiplication over finite fields of small characteristic. In [12], we have shown how to reduce multiplication in to multiplication in , while exploiting the fact that efficient DFTs are available over . Unfortunately, this reduction involves an overhead of a factor for zero padding. The Frobenius FFT can be thought of as a more direct way to use evaluation-interpolation strategies over for polynomial multiplication over . We have not yet implemented the algorithms in the present paper, but the ultimate hope is to make such multiplications twice as efficient as in [12].

Let us briefly detail the structure of this paper. In section 2, we first recall some standard complexity results for finite field arithmetic. We pursue with a quick survey of the fast Fourier transform (FFT) in section 3. The modern formulation of this algorithm is due to Cooley and Tukey [7], but the algorithm was already known to Gauß [9].

In section 4, we introduce the discrete Frobenius Fourier transform (DFFT) and describe several basic algorithms to compute DFFTs of prime order. There are essentially two efficient ways to deal with composite orders. Both approaches are reminiscent of analogue strategies for real FFTs [25], but the technical details are more complicated.

The first strategy is to reduce the computation of discrete Fourier transforms of polynomials of degree to a single discrete Fourier transform of a polynomial of degree . This elegant reduction is the subject of section 5, but it is only applicable if we need to compute many transforms. Furthermore, at best, it only leads to the gain of a factor instead of , due to numerous applications of the Frobenius automorphism .

The second strategy, to be detailed in section 6, is to carefully exploit the symmetries in the usual Cooley–Tukey FFT; we call the resulting algorithm the Frobenius FFT (FFFT). The complexity analysis is more intricate than for the algorithm from section 5 and the complete factor is only regained under certain conditions. Fortunately, these conditions are satisfied for the most interesting applications to polynomial multiplication in or when becomes very large with respect to .

In this paper, we focus on direct DFFTs and FFFTs. Inverse FFFTs can be computed in a straightforward way by reversing the main algorithm from section 6. Inverting the various algorithms from section 4 for DFFTs of prime orders requires a bit more effort, but should not raise any serious difficulties. The same holds for the algorithms to compute multiple DFFTs from section 5.

Besides inverse transforms, it is possible to consider truncated Frobenius FFTs, following [14, 15, 21], but this goes beyond the scope of this paper. It might also be worth investigating the use of Good's algorithm [10] as an alternative to the Cooley–Tukey FFT, since the order usually admits many distinct prime divisors in our setting.

2.Finite field arithmetic

Throughout this paper and unless stated otherwise, we will assume the bit complexity model for Turing machines with a finite number of tapes [23]. Given a field , we will write for the set of polynomials in of degree .

Let us write for some prime number and . Elements in the finite field are usually represented as elements of , where is a monic irreducible polynomial of degree . We will denote by the cost to multiply two polynomials in . Multiplications in can be reduced to a constant number of multiplications of polynomials in via the precomputation of a “pre-inverse” of . The multiplication of two polynomials in can be reduced to the multiplication of two polynomials in using Kronecker substitution [8, Corollary 8.27]. In other words, we have .

The best currently known bound for was established in [13]. It was shown there that


uniformly in , where

Sometimes, we rather need to perform multiplications with . The complexity of this operation will be denoted by . Throughout this paper, it will be convenient to assume that is increasing in both and . In other words, is increasing in both and .

The computation of the Frobenius automorphism in requires multiplications in when using binary powering, so this operation has complexity . This cost can be lowered by representing elements in with respect to so-called normal bases [5]. However, multiplication in becomes more expensive for this representation.

The discrete Frobenius Fourier transform potentially involves computations in all intermediate fields where divides . The necessary minimal polynomials for these fields and the corresponding pre-inverses can be kept in a table. The bitsize of this table is bounded by . It is known [22] that . Consequently, the bitsize of the table requires storage.

Another important operation in finite fields is modular composition: given and a monic polynomial of degree , the aim is to compute the remainder of the euclidean division of by . If is the minimal polynomial of the finite field , then this operation also corresponds to the evaluation of the polynomial at a point in . If and are two distinct monic irreducible polynomials in of degree , then the conversion of an element in to an element in also boils down to one modular composition of degree over .

We will denote by the cost of a modular composition as above. Theoretically speaking, Kedlaya and Umans proved that [19]. From a practical point of view, one rather has . If is smooth and one needs to compute several modular compositions for the same modulus, then algorithms of quasi-linear complexity do exist [17].

Given primitive elements and with , the paper [17] also contains efficient algorithms for conversions between and . Using modular composition, we notice that this problem reduces in time to the case where we are free to choose primitive elements and that suit us. We let be a cost function for conversions between and and denote .

3.Fast Fourier transforms

3.1The discrete Fourier transform

Let be any field and let be a primitive -th root of unity for some . Now consider a polynomial , where

The discrete Fourier transform of with respect to is the vector

If is a small number, then can be computed by evaluating directly for , using Horner's method for instance. If is a prime number, then Rader and Bluestein have given efficient methods for the computation of discrete Fourier transforms of order ; see [24, 6] and subsection 3.2 below.

In subsection 3.4, we are interested in the case when is composite. In that case, one may compute using the fast Fourier transform (or FFT). The modern formulation of this algorithm is due to Cooley and Tukey [7], but the algorithm was already known to Gauß [9]. The output of the FFT uses a mirrored indexation that we will introduce in subsection 3.3 below.

We will denote by the cost of computing a FFT in the field .

3.2Rader reduction

Assume that we wish to compute a DFT of large prime length . The multiplicative group is cyclic, of order . Let be such that is a generator of this cyclic group. Given a polynomial and with , we have


it follows that , when regarding , and as elements of

In other words, we have essentially reduced the computation of a discrete Fourier transform of length to a so-called cyclic convolution of length .

3.3Generalized bitwise mirrors

Let be a positive integer with a factorization such that for each . Then any index can uniquely be written in the form

where for all . We call the digits of when written in the mixed base . We also define the -mirror of by

Whenever is clear from the context, we simply write instead of .

If has length one, then we notice that

Setting , we also have

Finally, given , let , , and , so that and . Then it is not hard to see that for all and , we have

3.4The Cooley–Tukey FFT

We are now in a position to recall how the FFT works. Let , , and be as at the end of the previous subsection. We denote and notice that is a primitive -th root of unity. Similarly, is a primitive -th root of unity. Recall that for . For any and , we have

so that

In this formula, one recognizes two polynomial evaluations of orders and . More precisely, we may decompose the polynomial as

where and . This allows us to rewrite (3.1) as

For each , we may next introduce the polynomial by

The relation (3.2) now further simplifies into

This leads to the following recursive algorithm for the computation of discrete Fourier transforms.



Output: .

if then compute the answer using a direct algorithm and return it

Take , and

Let , ,


for do


for do





If the are all bounded, then this algorithm requires operations in . For practical purposes it is best though to pick such that and are as close to as possible. This approach is most “cache friendly” in the sense that it keeps data as close as possible in memory [2].

4.Frobenius Fourier transforms

4.1The discrete Frobenius Fourier transform

Let be an extension of finite fields. Let denote the Frobenius automorphism of over . We recall that the group generated by has size and that it coincides with the Galois group of over .

Let be a primitive -th root of unity with . Recall that and . We will write for the cyclic group of size generated by . The Frobenius automorphism naturally acts on and we denote by the order of an element under this action. Notice that . A subset is called a cross section if for every , there exists exactly one such that for some . We will denote this element by . We will also denote the corresponding set of indices by .

Now consider a polynomial . Recall that its discrete Fourier transform with respect to is defined to be the vector

Since admits coefficients in , we have

for all . For any , this means that we may retrieve from . Indeed, setting with , we obtain . We call

the discrete Frobenius Fourier transform of with respect to and the section .

Let and . Then leaves invariant, whence . Since is the subfield of that is fixed under the action of , this yields

It follows that the number of coefficients in needed to represent is given by

In particular, the output and input size of the discrete Frobenius Fourier transform coincide.

4.2Twiddled transforms

In the Cooley–Tukey algorithm from section 3.4, the second wave of FFTs operates on “twiddled” polynomials instead of the polynomials . An alternative point of view is to directly consider the evaluation of at the points with and where . We will call this operation a twiddled DFT.

The twiddled DFFT is defined in a similar manner. More precisely, assume that we are given an -th root of unity such that the set is stable under the action of . Assume also that we have a cross section of under this action and the corresponding set of indices . Then the twiddled DFFT computes the family

Again, we notice that

We will denote by the complexity of computing a twiddled DFFT of this kind.

4.3The naive strategy

If is a small number, then we recall from section 3.1 that it is most efficient to compute ordinary DFTs of order in a naive fashion, by evaluating for using Horner's method. From an implementation point of view, one may do this using specialized codelets for various small .

The same naive strategy can also be used for the computation of DFFTs and twiddled DFFTs. Given a cross section and the corresponding set of indices , it suffices to evaluate separately for each . Let be the order of under the action of . Then actually belongs to , so the evaluation of can be done in time . With the customary assumption that is increasing as a function of , it follows using (4.2) that the complete twiddled DFFT can be computed in time

Assuming that full DFTs of order over are also computed using the naive strategy, this yields

In other words, for small , we have achieved our goal to gain a factor .

4.4Full Frobenius action

The next interesting case for study is when is prime and the action of on is either transitive, or has only two orbits and one of them is trivial. If , then always forms an orbit of its own in , but we can have . If , then it can happen that is a singleton. This happens for instance for a -th primitive root of unity , for , and .

If is a singleton, say , then we necessarily have and the DFFT reduces to a single evaluation with and . This is precisely the problem of modular composition that we encountered in section 2, whence . The speed-up with respect to a full DFT is comprised between and , depending on the efficiency of our algorithm for modular composition. Theoretically speaking, we recall that , which allows us to gain a factor for any .

In a similar way, if and , then the computation of one DFFT reduces to additions (in order to compute ) and one composition modulo a polynomial of degree (instead of ).

Remark 4.1. Assume that is prime, and . If we are free to choose the representation of elements in , then the DFFT becomes particularly efficient if we take . In other words, if is the monic minimal polynomial of over (so that ), then we represent elements of as polynomials in modulo . Given , just writing down then yields the evaluation of at , whence .

4.5Rader reduction

For large prime orders and , let us now show how to adapt Rader reduction to the computation of DFFTs in favourable cases. With the notations from section 3.2, we have

for all , where is such that . The action of therefore induces an action on .

Let us assume for simplicity that the order of the additive subgroup generated by in is comprime with . Then there exists an element of order in and . Consequently,

We may thus regard the product as a bivariate polynomial in and . Let be the constant term in of this bivariate polynomial. From , we can recover for every . By construction, the with actually form a section of under the action of . This reduces the computation of a DFFT for the section to the computation of .

As to the computation of , assume for simplicity that contains a primitive -th root of unity. Recall that has coefficients in whereas admits coefficients in . Notice also that does not depend on the input , whence it can be precomputed. We compute as follows in the FFT-model with respect to . We first transform and into two vectors and . The computation of may again be regarded as a precomputation. The computation of the Fourier transform now comes down to modular compositions of degree over . We finally transform the result back into . Altogether, this yields

It is instructive to compare this with the cost of a full DFT of length over using Rader's algorithm:

Depending on the efficiency of modular composition, the speed-up for DFFTs therefore varies between and . Notice that the algorithm from the previous subsection becomes a special case with parameters and .

5.Multiple Fourier transforms

5.1Parallel lifting

Let be an extension of and assume that its elements are represented as polynomials in a primitive element of degree . Given polynomials , we may then form the polynomial

If and is a primitive -th root of unity in the base field , then we notice that

In other words, the discrete Fourier transform operates coefficientwise with respect to the basis of .

Recall that stands for the cost of computing Fourier transforms of length over . The maps and its inverse boil down to matrix transpositions in memory. On a Turing machine, they can therefore be computed in time . From (5.1), it follows that

This relation does not give us anything really new, since we made the assumption in section 2 that is increasing in both and . Nevertheless, it provides us with an elegant and explicit algorithm in a special case.

5.2Symmetric multiplexing

Let us next consider an -th root of unity and a primitive -th root of unity . Given , we have for all ,

Abbreviating and setting

it follows that

Now given the twiddled discrete Fourier transform of , we in particular know the values . Letting the Frobenius automorphism act on these values, we obtain the vector . Using one matrix-vector product , we may then retrieve the values of the individual twiddled transforms at . Doing this for each in a cross section of under the action of , this yields a way to retrieve the twiddled DFFTs of from the twiddled DFT of .

Recall that denotes the complexity of computing the DFFTs of elements of over . Since is a Vandermonde matrix, the matrix-vector product can be computed in time using polynomial interpolation [8, Chapter 10]. Given , we may compute each individual vector in time using the Frobenius automorphism . Since there are orbits in under the action of , we may thus retrieve the from in time . In other words,

5.3Multiplexing over an intermediate field

In the extreme case when , every has order one under the action of and . In that case, the bound (5.3) is not very sharp, but (5.2) already provides us with a good alternative bound for this special case. For with , let us now consider the case when has order at most under the action of for all , so that . Let be a primitive element in over , so that and . Given our polynomials , we may form

Then we have

Moreover, we may compute from for each , using the algorithm from the previous subsection, but with the additional property that at least one has maximal order under the action of .

If is prime and , then the above discussion shows that, without loss of generality, we may assume that has order under the action of . This means that has maximal order for every , whereas has order one. Hence, . Similarly, if is prime and , then we obtain a reduction to the case when has maximal order for all , whence . In both cases, we obtain the following:

Proposition 5.1. If is prime, then

Remark 5.2. We recall that , whence . When using the best known bound (2.1) for and , it follows for some constant that

In other words, we cannot hope to gain more than an asymptotic factor of with respect to a full DFT.

If is not necessarily prime, then the technique from this section can still be used for every individual . However, the rewritings of elements in as elements in and have to be done individually for each using modular compositions. Denoting by the order of under , it follows that

Notice that conversions of the same type correspond to modular compositions with the same modulus. If is smooth, then it follows that we may use the algorithms from [17] and keep the cost of the conversions quasi-linear.

6.The Frobenius FFT

Let and be as in section 3.3 and let be a primitive -th root of unity. In this section we present an analogue of the FFT from section 3.4: the Frobenius FFT (or FFFT). This algorithm uses the same mirrored indexation as the Cooley–Tukey FFT: given , it thus returns the family . We start with the description of the index set that corresponds to the so-called “privileged cross section” of .

6.1Privileged cross sections

Given , consider the orbit of under the action of . We define

Then is a cross section of under the action of ; we call it the privileged cross section for (and ).

Now let , , , and be as above. Then is a primitive -th root of unity. Let be the corresponding privileged cross section of .

Proposition 6.1. We have

Proof. Let and with and be such that for some . Since

and similarly , it follows that .

Inversely, given with for some , we claim that there exists a such that for . Indeed, implies that , whence is an -th root of unity. This means that there exists a with , i.e. .

Now assume that is minimal with for some . Given such that , the above claim shows that there exists a such that for . But then , whence . This shows that implies .

Inversely, assume that is minimal such that . Then the above claim implies that there exists a such that for . Without loss of generality we may assume that was chosen minimal while satisfying this property. Given , and with , we have . Consequently, and whenever . In other words, . This shows that implies the existence of a with and .

6.2The main algorithm

We are now in a position to adapt the Cooley–Tukey FFT. In the case when or is prime, we will use one of the algorithms from section 4 for the computation of twiddled DFFTs.



Output: .

if then return

Take , and

Let , ,



for do

for do


Notice that ,

where is the order of


if then






For the usual FFT, it was possible to choose in such a way that , and we recall that this improves the cache efficiency of practical implementations. However, this optimization is more problematic in our setting since it would require the development of an efficient recursive algorithm for twiddled FFFTs. This is an interesting topic, but beyond the scope of the present paper.

6.3Complexity analysis

Let us now perform the complexity analysis of . For , we first focus on all FFTs and twiddled DFFTs that are computed at “stage ” using a fallback algorithm. These FFTs and twiddled DFFTs are all of length . Given , let be the number of transforms of a polynomial in over . From (4.2), it follows that

Now a naive bound for the cost of an FFT or twiddled DFFT of a polynomial in over is

This means that the cost of all FFTs and twiddled DFFTs at stage is bounded by

Now can only be non zero if . Consequently,

The total cost of all FFTs and twiddled DFFTs of prime length is therefore bounded by

where .

Strictly speaking, if is a proper divisor of , then the output of a twiddled DFFT of a polynomial in over does not use the “standard” representation for , so the result must be converted. The cost of all such conversions at stage is bounded by

For a number with prime factorization , let . We also denote and notice that . Let us show by induction over that

This is clear for , since and for . Assuming that the relation holds for a given , we get

This completes the induction and we conclude that the total conversion cost is bounded by

This concludes the proof of the following result:

Theorem 6.2. If are all prime, then

where .

Remark 6.3. If the are very small, then we have shown in section 4.3 that (6.1) can be further sharpened into

As a consequence, the bound for becomes

Remark 6.4. It is also possible to treat the case when apart in the bound for , thereby avoiding the factor whenever possible. A similar analysis as for the cost of the conversions then yields

Whenever we can manage to keep and small with respect to , this means that we gain a factor with respect to a full DFT.

6.4Frobenius FFTs over

Let us now study the special case when and . This case is useful for the multiplication of polynomials with . The idea is to compute the Frobenius FFTs and , to perform the pointwise multiplications and to retrieve by doing one inverse Frobenius FFT.

Modulo zero padding, we have some flexibility in the choice of , which should divide . In particular, for polynomials and of large degree, we may choose to be a multiple of and take and (instead of ) for the top call of the algorithm FFFT from section 6.2. By selecting the representation of as explained in Remark 4.1, this means that for a single and for the remaining indices . In other words, one Frobenius FFT of length reduces to one Frobenius FFT of length and one full FFT over of length .

We choose the remaining to be small prime numbers, so as to minimize the cost of the full FFT over of length . For the remaining FFFT of length , we may then apply Theorem 6.2 and Remark 6.3. From a practical point of view, the computation of this remaining FFFT should take about times the time the computation of a full FFT of length for some small constant .

We conclude that one entire FFFT of length can be performed approximately times faster as a full FFT of length . We hope that this will lead to practical implementations for polynomial multiplication in that are approximately twice as efficient as the implementation from [12].


[1] L. Auslander, J. R. Johnson, and R. W. Johnson. An equivariant Fast Fourier Transform algorithm. Drexel University Technical Report DU-MCS-96-02, 1996.

[2] David H Bailey. Ffts in external of hierarchical memory. In Proceedings of the 1989 ACM/IEEE conference on Supercomputing, pages 234–242. ACM, 1989.

[3] G.D. Bergland. A fast Fourier transform algorithm for real-valued series. Communications of the ACM, 11(10):703–710, 1968.

[4] R. Bergmann. The fast Fourier transform and fast wavelet transform for patterns on the torus. Applied and Computational Harmonic Analysis, 2012. In Press.

[5] Dieter Blessenohl. On the normal basis theorem. Note di Matematica, 27(1):5–10, 2007.

[6] Leo I. Bluestein. A linear filtering approach to the computation of discrete Fourier transform. IEEE Transactions on Audio and Electroacoustics, 18(4):451–455, 1970.

[7] J.W. Cooley and J.W. Tukey. An algorithm for the machine calculation of complex Fourier series. Math. Computat., 19:297–301, 1965.

[8] J. von zur Gathen and J. Gerhard. Modern Computer Algebra. Cambridge University Press, New York, NY, USA, 3rd edition, 2013.

[9] C. F. Gauss. Nachlass: Theoria interpolationis methodo nova tractata. In Werke, volume 3, pages 265–330. Königliche Gesellschaft der Wissenschaften, Göttingen, 1866.

[10] I. J. Good. The interaction algorithm and practical Fourier analysis. Journal of the Royal Statistical Society, Series B. 20(2):361–372, 1958.

[11] A. Guessoum and R. Mersereau. Fast algorithms for the multidimensional discrete Fourier transform. IEEE Transactions on Acoustics, Speech and Signal Processing, 34(4):937–943, 1986.

[12] D. Harvey, J. van der Hoeven, and G. Lecerf. Fast polynomial multiplication over . In Proc. ISSAC '16, pages 255–262, New York, NY, USA, 2016. ACM.

[13] D. Harvey, J. van der Hoeven, and G. Lecerf. Faster polynomial multiplication over finite fields. J. ACM, 63(6), 2017. Article 52.

[14] J. van der Hoeven. The truncated Fourier transform and applications. In J. Gutierrez, editor, Proc. ISSAC 2004, pages 290–296, Univ. of Cantabria, Santander, Spain, July 4–7 2004.

[15] J. van der Hoeven. Notes on the Truncated Fourier Transform. Technical Report 2005-5, Université Paris-Sud, Orsay, France, 2005.

[16] J. van der Hoeven, R. Lebreton, and É. Schost. Structured FFT and TFT: symmetric and lattice polynomials. In Proc. ISSAC '13, pages 355–362, Boston, USA, June 2013.

[17] J. van der Hoeven and G. Lecerf. Modular composition via factorization. Technical report, HAL, 2017.

[18] J. Johnson and X. Xu. Generating symmetric DFTs and equivariant FFT algorithms. In ISSAC'07, pages 195–202. ACM, 2007.

[19] K. S. Kedlaya and C. Umans. Fast polynomial factorization and modular composition. SIAM J. Comput., 40(6):1767–1802, 2011.

[20] A Kudlicki, M. Rowicka, and Z. Otwinowski. The crystallographic Fast Fourier Transform. Recursive symmetry reduction. Acta Cryst., A63:465–480, 2007.

[21] R. Larrieu. The truncated fourier transform for mixed radices. Technical report, HAL, 2016.

[22] Jean-Louis Nicolas and Guy Robin. Highly composite numbers by Srinivasa Ramanujan. The Ramanujan Journal, 1(2):119–153, 1997.

[23] C.H. Papadimitriou. Computational Complexity. Addison-Wesley, 1994.

[24] C.M. Rader. Discrete Fourier transforms when the number of data samples is prime. Proc. IEEE, 56:1107–1108, 1968.

[25] H.V. Sorensen, D.L. Jones, M.T. Heideman, and C.S. Burrus. Real-valued fast Fourier transform algorithm. IEEE Transactions on Acoustics, Speech and Signal Processing, 35(6):849–863, 1987.

[26] L. F. Ten Eyck. Crystallographic Fast Fourier Transform. Acta Cryst., A29:183–191, 1973.

[27] A. Vince and X. Zheng. Computing the Discrete Fourier Transform on a hexagonal lattice. Journal of Mathematical Imaging and Vision, 28:125–133, 2007.