Under snowy weather conditions, cameras are prone to the interference of snow and can severely reduce the quality of the captured images, which will affect the computer vision performance greatly. Since no temporal information can be exploited, snow removal from single image is a challenging problem. In this paper, a novel snow removal method from single image was proposed by designing a kind of multi-scale image processing framework both in the spatial and frequency domain. Firstly, the input snowy image was decomposed into detailed sub-images and approximate parts by the Laplacian pyramid transform. Secondly, the approximate part is decomposed again into the background and detailed sub-image by the edge-preserving and structure-preserving image smoothing filter. After that, the non-subsampled shearlet transform was introduced to detect snowflakes within the frequency domain of the detailed sub-images, while mathematical morphological filtering was adopted to remove the labeled snowflakes within their spatial domain. Finally, the desnowing image was obtained by the inverse Laplacian pyramid transform. Experiments on real-world snowy images show that the proposed method produces better results than those of other state-of-the-art methods.
When capturing images in low-light conditions, the images are often degraded with low visibility and severe noise. To improve the visual quality and repress the noise simultaneously, a kind of low-light image enhancement method via layer decomposition and optimization was proposed. Firstly, the low-light image was smoothed via iterative least squares thus we could get the noise-free basic layer. Secondly, by means of subtraction of the original image and the basic layer we could get the detailed layer. Then we enhance the basic layer via variational Retinex-based method. At the meantime, we weaken the noise of the detailed layer by non-subsampled shearlet transform. Finally, we could obtain the enhanced image by fusion of the optimized basic layer and detailed layer. Experimental results of a number of low-light images reveal the efficiency of the proposed method and show its superiority over several state-of-the-arts.
The spatially modulated full polarization imaging technology can simultaneously acquire the target full polarization parameter, and the spectral aliasing and interference intensity existing in the polarization information demodulation result in low spatial resolution and false information in the frequency domain reconstruction target image. The polarization component images are reconstructed and restored by selecting filters of different bandwidths and matching the adaptive filters. The experimental results show that under different filter bandwidths, the system exhibits different modulation spectrum characteristics and is matched by filters. The design improves the image reconstruction restoration effect and provides reference for polarization detection and analysis research.
In order to study the spectral characteristics and polarization characteristics of the target and background, a high-spectral full-polarization imaging system design scheme was proposed and the system was built in the laboratory. The system is based on quarter-wave plate and liquid crystal tunable filter (LCTF) for spectral and full-polarization imaging. Polarization detection and spectral detection can be achieved by adjusting the angle of the quarter-wave plate and the exit wavelength of the LCTF. Specific detection methods. The hyperspectral full polarization detection of the system was verified in the range of 450nm to 710nm, and the data was analyzed and analyzed. The relationship between polarization characteristics and wavelength of different targets was analyzed, and the feasibility of the system design scheme was verified.
Low-light image enhancement is a challenging problem in the field of computer vision. In order to obtain more pleasing enhancement results, a low-light image enhancement method via joint convolutional sparse representation is proposed. The method is based on the Retinex theory and improves the problem of insufficient constraints. More concretely, when estimating illumination, the joint convolution sparse representation is proposed as structure and texture constraints to obtain a structural image severed as illumination. Then, the adaptive gradient constraint is used to enhance the details of the reflection image. Experiments on a number of challenging low-light images are present to reveal the efficacy of our method and show its superiority over several state-of-the-arts on both subjective and objective assessments.
In this paper, a novel enhancement algorithm for low-light images captured under low illumination conditions is proposed. More concretely, we design a method firstly to synthesize low-light images as training datasets. Then preclustering is conducted to separate training data into several groups by a coupled Gaussian mixture model. For each group, we adopt a coupled dictionary learning approach to train the low-light and normal-light dictionary pair jointly, and the statistical dependency of the sparsity coefficients is captured via Extreme Learning Machine simultaneously. Besides, we use a multi-phase dictionary learning strategy to enhance the robustness of our method. Experimental results show that proposed method is superior to existing methods.
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