### Table 5: The results of the super-resolution image reconstruction.

"... In PAGE 17: ...(0; 0); (0; 2); (1; 1); (1; 3); (2; 0); (2; 2); (3; 1); (3; 3)g, see Figure 5. As in x4.2.2, the tensor product of the lowpass lter m in Example 2 is used to generate the low-resolution images, and white noises at SNR = 40dB are added. Table5 shows the results of the least squares model, and Algorithms 2 and 3 with symmetric boundary conditions. The optimal in Step 2 and Step 4 are 0:0121 and 0:0105 for the least squares method and 0:0170 and 0:0161 for Algorithm 2 respectively.... ..."

### Table 5: The results of the super-resolution image reconstruction.

"... In PAGE 17: ...(0; 0); (0; 2); (1; 1); (1; 3); (2; 0); (2; 2); (3; 1); (3; 3)g, see Figure 5. As in x4.2.2, the tensor product of the lowpass lter m in Example 2 is used to generate the low-resolution images, and white noises at SNR = 40dB are added. Table5 shows the results of the least squares model, and Algorithms 2 and 3 with symmetric boundary conditions. The optimal in Step 2 and Step 4 are 0:0121 and 0:0105 for the least squares method and 0:0170 and 0:0161 for Algorithm 2 respectively.... ..."

### Table 4: Contrast and Image mottle enhancement from the original input image and for each deconvolu- tion method (enhancement expressed in percentage). Respectively; the Van-Cittert (VC), the Landweber (LW), the Richardson-Lucy (RL), the Super Resolution (SR), the Molina (MO), the IBD, the Biggs-Lucy (BL), the NAS-RIF and nally, the You-Kaveh apos;s (YK) algorithm.

"... In PAGE 16: ... Due to the di erence of thickness between the cross-sectional slices of the real and segmented phantom, these abovementioned measures are estimated on the whole 3D phantom after this one has been registered [22] on the ground truth of the segmented phantom volume (see Figure 10 where some consecutive slices of the segmented phantom are shown). Table4 gives the contrast and image mottle for each deconvolution technique applied on this SPECT phantom. Amongst the supervised deconvolution schemes, the Landweber apos;s algorithm allows to increase sig- ni cantly the contrast of the image but at cost of an unacceptable increase of the mottle of the image (+33:0% of mottle).... ..."

### Table 1: Iteration number for each supervised deconvolution method as chosen by the proposed stopping rule. Respectively; the Van-Cittert (VC), the Landweber (LW), the Richardson-Lucy (RL), the Super Resolution (SR) and the Molina apos;s (MO) algorithms.

"... In PAGE 14: ... For the unregularized supervised deconvolution methods, the termination criteria is given by the stopping strategy presented in Section 3.4 (see Table1 ). For the blind deconvolution methods requiring... ..."

### Table 12. System efficiencies using Thermal Diodes. System Description Efficiency

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### Table 3 Human classification of thermal images.

"... In PAGE 18: ... This type of experimentation is also reported by [5], which is evidence of how the lack of published results and comparable data-sets makes system comparisons difficult. The results for the thermal and visual image experi- ments are shown in Table3 and Table 4 respectively. The human classification ex- periments establish the extreme difficulty of the FER problem posed in this paper.... ..."