Laser & Optoelectronics Progress, Volume. 57, Issue 24, 241016(2020)
Blind Restoration of Multi-Channel Images Based on Total Variation and Dark Pixels
Fig. 1. Comparison of dark pixel sparseness between blurred image and clear image. (a) Clear image; (b) blurred image; (c) dark channel image of
Fig. 2. Target image estimation process
Fig. 3. Flowchart of PSF estimation algorithm
Fig. 4. Flowchart of MCBD algorithm
Fig. 5. “church” blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
Fig. 6. “clock” blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
Fig. 7. “architectural” blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
Fig. 8. “bridge” blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
Fig. 9. Real blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
Fig. 10. Real blurred images and restoration results. (a1)--(a3) Blurred images; (b) algorithm in Ref.[23]; (c) algorithm in Ref.[6]; (d) proposed algorithm
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Haoran Hu, Hui Liu, Huan Huang. Blind Restoration of Multi-Channel Images Based on Total Variation and Dark Pixels[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241016
Category: Image Processing
Received: Apr. 7, 2020
Accepted: Jun. 24, 2020
Published Online: Nov. 25, 2020
The Author Email: Liu Hui (liuhui@ynao.ac.cn)