Image Dehazing by An Artificial Image Fusion Method based on Adaptive Structure Decomposition
The project presents visibility restoration of single hazy images using color analysis and depth estimation with enhanced refined transmission technique. Visibility of outdoor images is often degraded by turbid mediums in poor weather, Haze can seriously affect the visible and visual quality of outdoor images. As a challenge in practice, image dehazing techniques are always used to remove haze from the captured images. Existing image dehazing algorithms focus on enhancing both global image contrast and saturation, but ignore the local enhancement. So the dehazed images do not often have good performance in the visual quality of local details. This paper proposes a new single-image dehazing solution based on the adaptive structure decomposition integrated multi-exposure image fusion A set of underexposed image sequences are extracted from a single blurred image first by a series of gamma correction and the spatial linear adjustment of saturation. Then different exposure-level images are fused into a haze-free image by applying a multi-exposure image fusion scheme based adaptive structure decomposition to each image patch. The proposed image dehazing scheme can effectively eliminate the visual degradation caused by haze without the physical model inversion of haze formation. Both apriori estimation of scene depth and the expensive refinement process of depth mapping can be avoided. The entropy of image texture named as texture energy is used to measure the image energy and obtain the information size contained in an image. Meanwhile, a texture energy based method is presented to adaptively select the corresponding patch size for the decomposition of image structure. In addition, this paper verifies that the dehazed images obtained by the patch based always meet the requirements of intensity decrease. The comparative experiment results are evaluated in both qualitative and quantitative aspects, which confirm the effectiveness of the proposed solution in haze removal.
- Additional Information approaches
- Retinex theory and Gamma correction
- Local contrast adjustment technique
- Dark channel prior method
- Difficult to acquire scene depth information
- Low performance in restoration of image quality
- It degrades image quality after restoration due to blocking artifacts.
- It doesn’t provide optimal transmission which causes halo effect and color distortion problems
- Visibility Restoration of single hazy images based on,
- Color Analysis and Depth Estimation with Enhanced refined transmission
- Fusion algorithm
Image Dehazing by An Artificial Image Fusion Method
- Depth Estimation
- Adaptive Gamma Correction
- Color Analysis
- Visibility Restoration
- It avoids halo effect and insufficient transmission estimation problems.
- It recovers better image quality under various weather condition changes.
- Less algorithm complexity.
- Its processing time is low.
- Advanced Driver Assistance System
- Video Surveillance systems
- Obstacle Detection systems
- Outdoor Object recognition systems
- MATLAB 2014 version
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