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author | dkazanc <dkazanc@hotmail.com> | 2019-12-13 17:57:21 +0000 |
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committer | dkazanc <dkazanc@hotmail.com> | 2019-12-13 17:57:21 +0000 |
commit | 1e9d5bc4ad830ff6ddb3cd9fbb8239d7a93b43be (patch) | |
tree | 4b5f18adc97ed89ff8164179f8c8397ccff1676a /src | |
parent | c9fa02f93c506dcbed963ad5b51202c071e3d53d (diff) | |
download | regularization-1e9d5bc4ad830ff6ddb3cd9fbb8239d7a93b43be.tar.gz regularization-1e9d5bc4ad830ff6ddb3cd9fbb8239d7a93b43be.tar.bz2 regularization-1e9d5bc4ad830ff6ddb3cd9fbb8239d7a93b43be.tar.xz regularization-1e9d5bc4ad830ff6ddb3cd9fbb8239d7a93b43be.zip |
initial modification of NDF
Diffstat (limited to 'src')
-rw-r--r-- | src/Core/regularisers_CPU/Diffusion_core.c | 88 | ||||
-rw-r--r-- | src/Core/regularisers_GPU/NonlDiff_GPU_core.cu | 48 |
2 files changed, 97 insertions, 39 deletions
diff --git a/src/Core/regularisers_CPU/Diffusion_core.c b/src/Core/regularisers_CPU/Diffusion_core.c index f0039c7..1a05c39 100644 --- a/src/Core/regularisers_CPU/Diffusion_core.c +++ b/src/Core/regularisers_CPU/Diffusion_core.c @@ -38,7 +38,7 @@ int signNDFc(float x) { * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion * 4. Number of iterations, for explicit scheme >= 150 is recommended * 5. tau - time-marching step for explicit scheme - * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight, 4 - Threshold-constrained Linear * 7. eplsilon - tolerance constant * * Output: @@ -60,14 +60,14 @@ float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float l re = 0.0f; re1 = 0.0f; int count = 0; DimTotal = (long)(dimX*dimY*dimZ); - + if (epsil != 0.0f) Output_prev = calloc(DimTotal, sizeof(float)); - + /* copy into output */ copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ)); - + for(i=0; i < iterationsNumb; i++) { - + if ((epsil != 0.0f) && (i % 5 == 0)) copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ)); if (dimZ == 1) { /* running 2D diffusion iterations */ @@ -93,7 +93,7 @@ float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float l if (count > 3) break; } } - + free(Output_prev); /*adding info into info_vector */ infovector[0] = (float)(i); /*iterations number (if stopped earlier based on tolerance)*/ @@ -109,7 +109,7 @@ float LinearDiff2D(float *Input, float *Output, float lambdaPar, float tau, long { long i,j,i1,i2,j1,j2,index; float e,w,n,s,e1,w1,n1,s1; - + #pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1) for(j=0; j<dimY; j++) { /* symmetric boundary conditions (Neuman) */ @@ -120,17 +120,17 @@ float LinearDiff2D(float *Input, float *Output, float lambdaPar, float tau, long i1 = i+1; if (i1 == dimX) i1 = i-1; i2 = i-1; if (i2 < 0) i2 = i+1; index = j*dimX+i; - + e = Output[j*dimX+i1]; w = Output[j*dimX+i2]; n = Output[j1*dimX+i]; s = Output[j2*dimX+i]; - + e1 = e - Output[index]; w1 = w - Output[index]; n1 = n - Output[index]; s1 = s - Output[index]; - + Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index])); }} return *Output; @@ -141,7 +141,7 @@ float NonLinearDiff2D(float *Input, float *Output, float lambdaPar, float sigmaP { long i,j,i1,i2,j1,j2,index; float e,w,n,s,e1,w1,n1,s1; - + #pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1) for(j=0; j<dimY; j++) { /* symmetric boundary conditions (Neuman) */ @@ -152,28 +152,28 @@ float NonLinearDiff2D(float *Input, float *Output, float lambdaPar, float sigmaP i1 = i+1; if (i1 == dimX) i1 = i-1; i2 = i-1; if (i2 < 0) i2 = i+1; index = j*dimX+i; - + e = Output[j*dimX+i1]; w = Output[j*dimX+i2]; n = Output[j1*dimX+i]; s = Output[j2*dimX+i]; - + e1 = e - Output[index]; w1 = w - Output[index]; n1 = n - Output[index]; s1 = s - Output[index]; - + if (penaltytype == 1){ /* Huber penalty */ if (fabs(e1) > sigmaPar) e1 = signNDFc(e1); else e1 = e1/sigmaPar; - + if (fabs(w1) > sigmaPar) w1 = signNDFc(w1); else w1 = w1/sigmaPar; - + if (fabs(n1) > sigmaPar) n1 = signNDFc(n1); else n1 = n1/sigmaPar; - + if (fabs(s1) > sigmaPar) s1 = signNDFc(s1); else s1 = s1/sigmaPar; } @@ -195,8 +195,18 @@ float NonLinearDiff2D(float *Input, float *Output, float lambdaPar, float sigmaP if (fabs(s1) <= sigmaPar) s1 = s1*powf((1.0f - powf((s1/sigmaPar),2)), 2); else s1 = 0.0f; } + else if (penaltytype == 4) { + /* Threshold-constrained linear diffusion + This means that the linear diffusion will be performed on pixels with + absolute difference less than the threshold. + */ + if (fabs(e1) > sigmaPar) e1 = 0.0f; + if (fabs(w1) > sigmaPar) w1 = 0.0f; + if (fabs(n1) > sigmaPar) n1 = 0.0f; + if (fabs(s1) > sigmaPar) s1 = 0.0f; + } else { - printf("%s \n", "No penalty function selected! Use 1,2 or 3."); + printf("%s \n", "No penalty function selected! Use 1,2,3 or 4."); break; } Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index])); @@ -211,7 +221,7 @@ float LinearDiff3D(float *Input, float *Output, float lambdaPar, float tau, long { long i,j,k,i1,i2,j1,j2,k1,k2,index; float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1; - + #pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d) for(k=0; k<dimZ; k++) { k1 = k+1; if (k1 == dimZ) k1 = k-1; @@ -225,21 +235,21 @@ float LinearDiff3D(float *Input, float *Output, float lambdaPar, float tau, long i1 = i+1; if (i1 == dimX) i1 = i-1; i2 = i-1; if (i2 < 0) i2 = i+1; index = (dimX*dimY)*k + j*dimX+i; - + e = Output[(dimX*dimY)*k + j*dimX+i1]; w = Output[(dimX*dimY)*k + j*dimX+i2]; n = Output[(dimX*dimY)*k + j1*dimX+i]; s = Output[(dimX*dimY)*k + j2*dimX+i]; u = Output[(dimX*dimY)*k1 + j*dimX+i]; d = Output[(dimX*dimY)*k2 + j*dimX+i]; - + e1 = e - Output[index]; w1 = w - Output[index]; n1 = n - Output[index]; s1 = s - Output[index]; u1 = u - Output[index]; d1 = d - Output[index]; - + Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index])); }}} return *Output; @@ -249,7 +259,7 @@ float NonLinearDiff3D(float *Input, float *Output, float lambdaPar, float sigmaP { long i,j,k,i1,i2,j1,j2,k1,k2,index; float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1; - + #pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d) for(k=0; k<dimZ; k++) { k1 = k+1; if (k1 == dimZ) k1 = k-1; @@ -263,38 +273,38 @@ float NonLinearDiff3D(float *Input, float *Output, float lambdaPar, float sigmaP i1 = i+1; if (i1 == dimX) i1 = i-1; i2 = i-1; if (i2 < 0) i2 = i+1; index = (dimX*dimY)*k + j*dimX+i; - + e = Output[(dimX*dimY)*k + j*dimX+i1]; w = Output[(dimX*dimY)*k + j*dimX+i2]; n = Output[(dimX*dimY)*k + j1*dimX+i]; s = Output[(dimX*dimY)*k + j2*dimX+i]; u = Output[(dimX*dimY)*k1 + j*dimX+i]; d = Output[(dimX*dimY)*k2 + j*dimX+i]; - + e1 = e - Output[index]; w1 = w - Output[index]; n1 = n - Output[index]; s1 = s - Output[index]; u1 = u - Output[index]; d1 = d - Output[index]; - + if (penaltytype == 1){ /* Huber penalty */ if (fabs(e1) > sigmaPar) e1 = signNDFc(e1); else e1 = e1/sigmaPar; - + if (fabs(w1) > sigmaPar) w1 = signNDFc(w1); else w1 = w1/sigmaPar; - + if (fabs(n1) > sigmaPar) n1 = signNDFc(n1); else n1 = n1/sigmaPar; - + if (fabs(s1) > sigmaPar) s1 = signNDFc(s1); else s1 = s1/sigmaPar; - + if (fabs(u1) > sigmaPar) u1 = signNDFc(u1); else u1 = u1/sigmaPar; - + if (fabs(d1) > sigmaPar) d1 = signNDFc(d1); else d1 = d1/sigmaPar; } @@ -322,11 +332,23 @@ float NonLinearDiff3D(float *Input, float *Output, float lambdaPar, float sigmaP if (fabs(d1) <= sigmaPar) d1 = d1*powf((1.0f - powf((d1/sigmaPar),2)), 2); else d1 = 0.0f; } + else if (penaltytype == 4) { + /* Threshold-constrained linear diffusion + This means that the linear diffusion will be performed on pixels with + absolute difference less than the threshold. + */ + if (fabs(e1) > sigmaPar) e1 = 0.0f; + if (fabs(w1) > sigmaPar) w1 = 0.0f; + if (fabs(n1) > sigmaPar) n1 = 0.0f; + if (fabs(s1) > sigmaPar) s1 = 0.0f; + if (fabs(u1) > sigmaPar) u1 = 0.0f; + if (fabs(d1) > sigmaPar) d1 = 0.0f; + } else { - printf("%s \n", "No penalty function selected! Use 1,2 or 3."); + printf("%s \n", "No penalty function selected! Use 1,2,3 or 4."); break; } - + Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index])); }}} return *Output; diff --git a/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu b/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu index de9abd4..a8876cd 100644 --- a/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu +++ b/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu @@ -32,7 +32,7 @@ limitations under the License. * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion * 4. Number of iterations, for explicit scheme >= 150 is recommended * 5. tau - time-marching step for explicit scheme - * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight, 4 - Threshold-constrained Linear * 7. eplsilon: tolerance constant * Output: @@ -108,8 +108,8 @@ __global__ void LinearDiff2D_kernel(float *Input, float *Output, float lambdaPar if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) { /* boundary conditions (Neumann reflections) */ - i1 = i+1; if (i1 == N) i1 = i-1; - i2 = i-1; if (i2 < 0) i2 = i+1; + i1 = i+1; if (i1 == N) i1 = i-1; + i2 = i-1; if (i2 < 0) i2 = i+1; j1 = j+1; if (j1 == M) j1 = j-1; j2 = j-1; if (j2 < 0) j2 = j+1; @@ -155,7 +155,32 @@ __global__ void LinearDiff2D_kernel(float *Input, float *Output, float lambdaPar if (abs(s1) <= sigmaPar) s1 = s1*pow((1.0f - pow((s1/sigmaPar),2)), 2); else s1 = 0.0f; } - else printf("%s \n", "No penalty function selected! Use 1,2 or 3."); + else if (penaltytype == 4) { + /* Threshold-constrained linear diffusion + This means that the linear diffusion will be performed on pixels with + absolute difference less than the threshold. + */ + if (abs(e1) <= 1.5f*sigmaPar) { + if (abs(e1) > sigmaPar) e1 = signNDF(e1); + else e1 = e1/sigmaPar;} + else e1 = 0.0f; + + if (abs(w1) <= 1.5f*sigmaPar) { + if (abs(w1) > sigmaPar) w1 = signNDF(w1); + else w1 = w1/sigmaPar;} + else w1 = 0.0f; + + if (abs(n1) <= 1.5f*sigmaPar) { + if (abs(n1) > sigmaPar) n1 = signNDF(n1); + else n1 = n1/sigmaPar; } + else n1 = 0.0f; + + if (abs(s1) <= 1.5f*sigmaPar) { + if (abs(s1) > sigmaPar) s1 = signNDF(s1); + else s1 = s1/sigmaPar; } + else s1 = 0.0f; + } + else printf("%s \n", "No penalty function selected! Use 1,2,3 or 4."); Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index])); } @@ -281,8 +306,19 @@ __global__ void NonLinearDiff3D_kernel(float *Input, float *Output, float lambda if (abs(d1) <= sigmaPar) d1 = d1*pow((1.0f - pow((d1/sigmaPar),2)), 2); else d1 = 0.0f; } - else printf("%s \n", "No penalty function selected! Use 1,2 or 3."); - + else if (penaltytype == 4) { + /* Threshold-constrained linear diffusion + This means that the linear diffusion will be performed on pixels with + absolute difference less than the threshold. + */ + if (abs(e1) > sigmaPar) e1 = 0.0f; + if (abs(w1) > sigmaPar) w1 = 0.0f; + if (abs(n1) > sigmaPar) n1 = 0.0f; + if (abs(s1) > sigmaPar) s1 = 0.0f; + if (abs(u1) > sigmaPar) u1 = 0.0f; + if (abs(d1) > sigmaPar) d1 = 0.0f; + } + else printf("%s \n", "No penalty function selected! Use 1,2,3 or 4."); Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index])); } } |