h"p://icv.ims.ut.ee ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡shb@ut.ee ¡
Conventional Image Enhancement to High Dynamic Range Image Enhancement Assoc. Prof. Dr. Gholamreza Anbarjafari Shahab iCV Research Group
Image Enhancement Resolution Enhancement Illumination Enhancement Denoising
• In visual perception of the real world, contrast is determined by the difference in the color and brightness of the object with other objects in the same field of view. • The human visual system is more sensitive to contrast than absolute luminance; hence, we can perceive the world similarly regardless of the considerable changes in illumination conditions.
2500 2000 1500 1000 500 0 0 50 100 150 200 250 (a) (b) 3000 2500 2000 1500 1000 500 0 0 50 100 150 200 250 (c) (d) A face image from the CALTECH face database (a), its histogram (b), the equalized face image using GHE (c) and its respective histogram (d).
ILLUMINATION (a) (b) A face image from the CALTECH face database (a), and the equalized face image using histogram equalization in each R, G, and B channels separately (b).
SINGULAR VALUE DECOMPOSITION T A U V = Σ A A A where U A and V A are orthogonal square matrices known as hanger and aligner respectively, and Σ A matrix contains the sorted singular values on its main diagonal. Σ A contains the intensity information of the given image
A grey scale image (a) (a) (b) and the effect of changing the σ 1 : σ 1 =0 (b), σ 1 = σ 1 +3 √σ 1 (c), σ 1 = σ 1 -3 √σ 1 (d), (c) (d) σ 1 = σ 1 +10 √σ 1 (e), σ 1 = σ 1 -10 √σ 1 (f), σ 1 = σ 1 +0.75 σ 1 (g), and σ 1 = σ 1 -0.75 σ 1 (h). (e) (f) (g) (h)
SVD BASED EQUAL İ ZAT İ ON: SVE T A U V = Σ A A A ( ) max Σ N 0,var 1 ( ) µ = = ξ = max ( ) Σ A T U V ( ) Ξ = ξ Σ equalized A A A A
SVE (a) (b) (c) (d) 1000 2000 800 1000 700 800 800 1500 600 500 600 600 1000 400 400 400 300 500 200 200 200 100 0 0 0 0 0 100 200 300 0 100 200 300 0 100 200 300 0 100 200 300 (e) (f) (g) (h) A face image from Caltech database (a), introduced low density of the same image (b) and the resultant image of SVE (c) and GHE (d) and their respective smoothed histograms (e)-(h).
W Avelet Discrete Wavelet Transform Single Tree Complex Wavelet Transform Dual Tree Complex Wavelet Transform 1 Level DWT
DWT
DWT+SVE LL subband concentrates the illumination information There are two significant parts of the proposed method: • The first one is the use of SVD. Changing singular values will directly affect the illumination of the image hence the other information in the image will not be changed. • The second important aspect of this work is the application of DWT.
Low contrast input satellite image DWT+SVE Equalized image using GHE DWT DWT ( ) max Σ LL ˆ HH HL LH LL LL LH HL HH A ζ = ( ) Calculate the U, Σ , and V for Calculate the U, Σ , and V for max Σ LL A LL subband image and find LL subband image and find the maximum element in Σ . the maximum element in Σ . Calculate ζ using Eq (4) Calculate the new Σ and reconstruct the new LL = Σ Σ LL A ζ image, by using Eq (6). LL A LL U V = Σ LL A IDWT A A A LL LL Equalized satellite image
DWT+SVE (a) (b) (c) (d) (e) (f) ¡ Original low contrast images from Antarctic Meteorological Research Centre (a), equalized image by using: GHE (b), LHE (c), SVE (d), BPDHE (e), and proposed technique (f).
DWT+SVE (a) (b) (c) (d) (e) (f) ¡ Original low contrast image from Satellite imaging Corporation (a), equalized image by using: GHE (b), LHE (c), SVE (d), BPDHE (e), and proposed technique (f).
P Ublished Work 1. Demirel, H., & Anbarjafari, G. (2008). Pose invariant face recognition using probability distribution functions in different color channels. Signal Processing Letters, IEEE Signal Processing Letters, IEEE , 15 , 537-540. 2. Demirel, H., Anbarjafari, G., & Jahromi, M. N. S. (2008, October). Image equalization based on singular value decomposition. In In Computer Computer and and Information Information Sciences Sciences, 2008. ISCIS'08. 23rd International Symposium on (pp. 1-5). IEEE IEEE. 3. Demirel, H., Ozcinar, C., & Anbarjafari, G. (2010). Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. Geoscience Geoscience and and Remote Remote Sensing Sensing Letters, IEEE Letters, IEEE , 7 (2), 333-337. 4. Anbarjafari, G., Jafari, A., Jahromi, M. N. S., Ozcinar, C., & Demirel, H. (2015). Image illumination enhancement with an objective no- reference measure of illumination assessment based on Gaussian distribution mapping. Engineering Engineering Science Science and and Technology Technology, an International Journal , 18 (4), 696-703.
P Ublished Work 5. Ozcinar, C., Demirel, H., & Anbarjafari, G. (2011). Image Equalization Using Singular Value Decomposition and Discrete Wavelet Transform. Discrete Discrete Wavelet Wavelet Transforms: Transforms: Theory Theory and and Applications Applications , 87-94. 6. Anbarjafari, G., Izadpanahi, S., & Demirel, H. (2015). Video resolution enhancement by using discrete and stationary wavelet transforms with illumination compensation. Signal, Signal, Image Image and and Video Video Processing Processing , 9 (1), 87-92. 7. Demirel, H., Anbarjafari, G., Ozcinar, C., & Izadpanahi, S. (2011, September). Video resolution enhancement by using complex wavelet transform. In Image Image Processing Processing (ICIP), 2011 18th IEEE International Conference on (pp. 2093-2096). IEEE IEEE.
HDR • High Dynamic Range Imaging • 10-12-14-16-… bits • Displays are conventional 8-10 bits • Standards?
HDR • Collaborative work with Telecom ParisTech for ICIP2016 • Adaptive HDR display • Reduction of flickers
HDR • Demo 1 • Demo 2
HDR
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