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Thermal image formation can be modeled as the convolution of an ideal image with a point spread function (PSF) that characterizes the optical degradations. Although simple space-invariant models are sufficient to model diffraction-limited optical systems, they cannot capture local variations that arise because of nonuniform blur. Such degradations are common when the depth of field is limited or when the scene involves motion. Although space-variant deconvolution methods exist, they often require knowledge of the local PSF. In this paper, we adapt a local PSF estimation method (based on a learning approach and initially designed for visible light microscopy) to thermal images. The architecture of our model uses a ResNet-34 convolutional neural network (CNN) that we trained on a large thermal image dataset (CVC-14) that we split in training, tuning, and evaluation subsets. We annotated the sets by synthetically blurring sharp patches in the images with PSFs whose parameters covered a range of values, thereby producing pairs of sharp and blurred images, which could be used for supervised training and ground truth evaluation. We observe that our method is efficient at recovering PSFs when their width is larger than the size of a pixel. The estimation accuracy depends on the careful selection of training images that contain a wide range of spatial frequencies. In conclusion, while local PSF parameter estimation via a trained CNN can be efficient and versatile, it requires selecting a large and varied training dataset. Local deconvolution methods for thermal images could benefit from our proposed PSF estimation method.
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