A humidity sensor based on Fabry-Perot interferometer (FPI) and intracavity sensing of a fiber ring laser is proposed and experimentally demonstrated. A compact humidity-sensitive fiber-optic FPI is developed and inserted in a fiber ring laser. Because the output power of the fiber laser is modulated by the reflection loss of the FPI at different ambient humidity, intracavity humidity sensing is induced. The experiment shows that the relative output power of the fiber laser has a good linear response to ambient humidity from 25 %RH to 95 %RH and the humidity sensitivity of 0.202 dB/%RH is obtained with a narrow 3-dB bandwidth and high signal-to-noise ratio (SNR). Furthermore, the excellent performance of the sensor for remote humidity detection is demonstrated. The sensor also shows a low temperature cross-sensitivity, fast time response, and good stability. The proposed sensor has a great potential in high-capacity sensor network and remote detections.

When light traverses through water, it undergoes influence from the absorption and scattering of particles, resulting in diminished contrast and color distortion within underwater imaging. These effects further constrain the observation of underwater environments and the extraction of features from submerged objects. To address these challenges, we introduce an underwater color image processing approach, which amalgamates the frequency and spatial domains, enhancing image contrast in the frequency domain, adaptively refining image color within the spatial domain, and ultimately merging the contrast-enhanced image with the color-corrected counterpart within the CIE L*a*b* color space. Experiments conducted on standard underwater image benchmark datasets highlight the significant improvements our proposed method achieves in terms of enhancing contrast and rendering more natural colors compared to several state-of-the-art methods. The results are further evaluated using four commonly used image metrics, consistently showing that our method yields the highest average value. The proposed method effectively addresses challenges related to low contrast, color distortion, and obscured details in underwater images, a fact especially evident in various scenarios involving color-affected underwater imagery.