Detail-Enhanced Exposure Fusion

Transcription

1 Detail-Enhanced Exposure Fusion IEEE Transactions on Consumer Electronics Vol. 21, No.11, November 2012 Zheng Guo Li, Jing Hong Zheng, Susanto Rahardja Presented by Ji-Heon Lee School of Electrical Engineering and Computer Science Kyungpook National Univ.

2 Exposure fusion Abstract Obtaining pseudo-hdri without generation of ture HDRI Proposed method Detail-enhanced weight Novel quadratic optimization-based method Extracting fine details from LDRIs Fusion fine details 2/17

3 Tone mapping E.Reinhard s Introduction Obtaining pseudo-hdr image from true-hdr image Farbman s method Correction of filter in Retinnex Theory Based on Global Filter Based on Edge-Preserving Filter General flow Compression Filter Input image Three detail layers function Output luminance Restore color 3/17

4 Exposure fusion Mertens method Considering contrast, saturation and well-exposure weight Fusion of LDR images using pyramid method Zhang s method Using gradient direction for ghost-free 4/17

5 Overview of proposed method Using quadratic optimization-based method Obtaining fine details Using T.Mertens method for restriction of well-pixel Contrast, saturation, well-exposedness Fusion of LDR images with pyramid method Restricting fused image using fine-detail weight 5/17

6 Detail-enhanced fusion of differently exposed images Vector field Gradient field ( Y ( i, j 1) Y ( i, j), Y ( i 1, j) Y ( i, j)) k k k where Y ( i, j)(1 k N) k is luma components. Well-exposure restriction where ( z) z 1; if z 127 ( z) 256 z; otherwise is weight function. Weight factor of gradient vector W ( i, j) ( Y ( i, j)) ( Y ( i, j 1)) k,1 k k W ( i, j) ( Y ( i, j)) ( Y ( i 1, j)) k,2 k k ( Y ( i, j), Y ( i, j)) k,1 k,2 (1) (2) (3) 6/17

7 Vector field Constructing by weighted average of gradients over all exposures v q N k 1 W k, q k, q N k 1 log( Y ), q 1,2 W kq, (4) where v( i, j) ( v1( i, j), v2( i, j)) T stand for desired vectors, v, W, Y is vectors of vq ( i, j)' s, Wk, q ( i, j)' s, Yk, q( i, j)' s. q k, q k, q 7/17

8 Fine details weight min Ld L d L L d v1 v2 x y ( v ) ( v ) d 2 (5) where is l 2 2norm, Euclidean distance, Ld ( i, j) is represents fine details to be extracted at position ( i, j), L d is vector containing all Ld ( i, j)' s, function ( z) selected as ( z) z, is regularization factor which obtaining tradeoff between two terms. (6) 8/17

9 Optimal solution of fine details using following equation I D A( v ) D D A( v ) D L D A( v ) v D A( v ) v (7) T T T T x 1 x y 2 y d x 1 1 y 2 2 where D,and D are discrete differentiation operators, x y 1 1 A( v and are and. 1) A( v2) diag diag ( v1 ( i, j)) ( v2( i, j)) 9/17

10 T.Mertens method for restriction of well-pixel Weight sum of LDR images using pyramid method Obtaining weight Wk ( i, j) Ck ( i, j) Sk ( i, j) Ek ( i, j) C ( i, j) Contrast Saturation Sk ( i, j) Well-exposedness k E ( i, j) Fusing image operation of pyramid level k where N l l l L{ Z( i, j)} [ L{ Zk( i, j)} G{ Wk( i, j)} ] G{ W (, )} l k i j L{ Z ( i, j)} l k L{ Z( i, j)} l k 1 is weight map Gaussian pyramid, is Laplacian pyramid of LDR images, is fusion image Laplacian pyramid. (8) 10/17

11 Final fusion Combining fine detail weight and T.Mertens well-pixel weight where Z ( i, j) int Z ( i, j) Z ( i, j)exp( L ( i, j)) f int d is intermediate image generated by T.Mertens method. (9) 11/17

12 Experimental results Comparison of different selection of (a) (b) (c) (d) (e) (f) (g) (h) (f) Fig. 1. Comparison of different selections of λ. The input images are captured under the same lighting conditions. Image courtesy of Jacques Joffre. (a) First input image. (b) Second input image. (c) Third input image. (d) Details extracted by λ = (e) Details extracted by λ = 1. (f) Details extracted by λ = 4. (g) Final image obtained by λ = (h) Final image obtained by λ = 1. (i) Final image obtained by λ = 4. 12/17

13 Comparison with other methods Input image courtesy of Laurance Meylan (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) Fig. 2. Comparison of the proposed exposure fusion scheme with two multiple-scale exposure fusion schemes in [4] and [6]. Image courtesy of Laurance Meylan. (a) First input image. (b) Second input image. (c) Third input image. (d) Fourth input image. (e) Fifth input image. (f) Sixth input image. (g) Seventh input image. (h) Final image obtained by the exposure fusion scheme in [4]. (i) Final image obtained by the proposed fusion algorithm. (j) Final image obtained by the exposure fusion scheme in [6]. 13/17

14 (a) Input image courtesy of Jacques Joffre (a) (b) (c) (d) (e) (f) Fig. 3. Comparison of the proposed exposure fusion scheme with two multiple-scale exposure fusion schemes in [4] and [6]. Image courtesy of Jacques Joffre. (a) First input image. (b) Second input image. (c) Third input image. (d) Final image obtained by the exposure fusion scheme in [4]. (e) Final image obtained by the proposed exposure fusion scheme. (f) Final image obtained by the exposure fusion scheme in [6]. 14/17

15 Input image with flash image (a) (b) (c) (d) (e) (f) Fig. 4. Comparison of the proposed exposure fusion scheme with the HDR imaging scheme of Photoshop CS5 and the exposure fusion scheme in [4]. (a) Input image without flash. (b) Input image with flash. (c) Details extracted by the proposed exposure fusion scheme. (d) Final image obtained by the HDR imaging scheme in Photoshop CS5. (e) Final image obtained by the proposed exposure fusion scheme. (f) Final image obtained by the exposure fusion scheme in [4]. 15/17

16 Comparison focus on number of lighting resource (a) (b) (c) (d) (e) Fig. 4. Comparison of the proposed exposure fusion scheme with the HDR imaging schemes of Photoshop CS5 and the exposure fusion scheme in [4]. (a) Input image with one lighting resource. (b) Input image with two lighting resources. (c) Input image with three lighting resources. (d) Final image obtained by the HDR imaging scheme in Photoshop CS5. (e) Final image obtained by the proposed exposure fusion scheme. (f) Final image obtained by the exposure fusion scheme in [4]. (f) 16/17

17 Proposed method Obtaining displayable HDR image without tonemapping Novel quadratic optimization-based method Extracting fine details from LDRIs Fusion fine details Experimental results Detail enhancement Conclusions More pleasing resulting than previous methods 17/17