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The Degree of Linear Polarization (DoLP) is a key dimension of information in polarization imaging. The quality of polarization images is greatly affected by imaging equipment and environmental factors. Therefore, an enhancement method for the DoLP image is proposed in this paper. First, a super-resolution reconstruction algorithm based on the Detail Enhancement Guided Image Filter is proposed to solve the problem of mosaicization and pseudo-polarization information caused by the degradation of spatial resolution and super-resolution reconstruction of DoFP imaging. Second, for the problem of rapid degradation of polarization image quality caused by hazy environment, a DoLP enhancement algorithm based on multi-channel difference Signal-to-Noise Ratio is proposed to improve the visual effect of polarization images in different scenes with hazy environment. The method proposed in this paper implements super-resolution reconstruction, demosaicing, pseudo-polarization information removal, and adaptive enhancement of DoLP images in DoFP systems. [ABSTRACT FROM AUTHOR]

With the rapid development of infrared technology, infrared cameras are widely used in the military, medical, and industrial fields. However, the thermal radiation information received by infrared cameras often undergoes degradation due to random factors such as scattering or reflection during transmission. Consequently, the final infrared image suffers from poor contrast and blurred vision. To address these problems, this paper proposes an infrared image contrast enhancement algorithm based on the atmospheric scattering model. Firstly, we modify the hypothesis of the dark channel prior theory, and the pseudo dark channel prior theory is deduced to meet the processing requirements of single-channel images. Secondly, we propose anti-target interference and filtering threshold method to accurately estimate the atmospheric illumination value and transmittance of the atmospheric scattering model. These methods effectively mitigate the influence of infrared targets on the atmospheric illumination value and eliminate the blocking artifact effect caused by pixel value jumps in the transmittance images. Furthermore, we employ adaptive histogram equalization with contrast limitation, highlighting the global contrast. This paper conducts experiments on the M3FD dataset and employs objective evaluations using Visible Edge (E), Figure Definition (FD), Peak Signal-to-Noise Ratio (PSNR), Information Entropy (IE), Structure Similarity Index Measure (SSIM), and Visual Information Fidelity (VIF). The results indicate that the proposed algorithm enhances the overall contrast of images while highlighting fine texture details, exhibiting good visual effects. Both subjective and objective evaluations outperform seven other contrast algorithms, demonstrating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]