Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

Abstract: Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

Abstract: Aspects of the disclosure relate generally to determine specularity of an object. As an example an object or area of geometry may be selected. A set of images that include the area of geometry may be captured. This set of images may be filtered to remove images that do not show the area of geometry well, such as if the area is in a shadow or occluded by another object. A set of intensity values for the area are determined for each image. A set of angle values for each image is determined based on at least a direction of a camera that captured the particular image when the particular image was captured. The set of average intensities and the set of angle values are paired and fit to a curve. The specularity of the area may then be classified based on at least the fit.

Abstract: Aspects of the disclosure relate generally to determine specularity of an object. As an example an object or area of geometry may be selected. A set of images that include the area of geometry may be captured. This set of images may be filtered to remove images that do not show the area of geometry well, such as if the area is in a shadow or occluded by another object. A set of intensity values for the area are determined for each image. A set of angle values for each image is determined based on at least a direction of a camera that captured the particular image when the particular image was captured. The set of average intensities and the set of angle values are paired and fit to a curve. The specularity of the area may then be classified based on at least the fit.

Abstract: Aspects of the disclosure relate generally to determine specularity of an object. As an example an object or area of geometry may be selected. A set of images that include the area of geometry may be captured. This set of images may be filtered to remove images that do not show the area of geometry well, such as if the area is in a shadow or occluded by another object. A set of intensity values for the area are determined for each image. A set of angle values for each image is determined based on at least a direction of a camera that captured the particular image when the particular image was captured. The set of average intensities and the set of angle values are paired and fit to a curve. The specularity of the area may then be classified based on at least the fit.