Abstract
In general, we expect to receive vivid image, and further apply computer vision image processing on it. However, weather varies with time. Haze lowers hue saturation, degrades contrast, and affects recognition of human vision and computer vision. There exists many dehazing methods. Among them, the dark channel prior method displays impressive result, but it still cost 10~20 seconds for a 400*600 image. Therefore, we utilize it assumption and try to reduce its most time-consuming step while producing acceptable output. During the modification, we encounter the inevitable color shift problem, which is solved by recovering the optical model to its original form. In rain removal part, most current methods need video as input. Motion between frames are used to detect possible candidate rain streaks and more strict constraints are applied to further filtered out non-rain object. Blur and temporal median filter are implemented on those rain streaks. Each of these methods shows amazing results, but they fail to achieve rain removal in single image. For heavy rain, we propose a different method from the past. By viewing rain streaks as texture, it meets the image decomposition idea and MCA framework is adopted. But the dictionary is not orthogonal, thus we have to select exemplar patches from test image and train a corresponding dictionary.