This letter presents a new framework for improving the spatial resolution of infrared (IR) images based on the high-resolution visible image information. Edge regions in an IR image, which have a strong correlation with the aligned edges in the visible image, are sharpened by using their high frequency patches, which are locally estimated from the visible image. The estimation is performed on the basis of intensity correlations between two images. In addition, in order to improve the resolution in the uncorrelated edge regions and the texture regions where visible image information is not available, we adopt learning-based and reconstruction-based super resolution algorithms, respectively. Experimental results demonstrate that the proposed algorithm improves the spatial resolution compared with the existing upsampling algorithms.

This paper presents the categorization and restoration of defective lines developed in pushbroom images. About 100 of the 3000 SPOT 4 SWIR detectors malfunction, which degrades image quality. Conventional methods have difficulties in effectively detecting and restoring defective lines, because they ignore the heterogeneity of the ground surface and the presence of sporadically unstable detectors with gain and offset that vary during a scan. While all defective lines have previously been considered as a single type, here they are categorized into three types according to the variation pattern in the scanning direction: constant defective lines, irregular defective lines, and irrecoverable defective lines. The detection procedure utilizes summed data and standard deviation data that consist of abnormal peaks originating from defective lines and a slowly varying baseline reflecting the surface characteristics within the image. The defective lines are detected by finding abnormal peaks, and classified and restored by using either a moment-matching method or interpolation, depending upon their types. Three SPOT 4 images were used to test and evaluate the performance of the proposed method. From the test results, the constant defective line was the most common type, comprising about 60%, while the irregular defective lines caused serious image degradation because of the difficulty of detecting and classifying them. Commission and omission errors were less than 10% and detection accuracy was higher than 90%. The analysis of signal-to-noise ratio (SNR) showed that the low SNR created by the defective lines was effectively removed. Our method gave a significant improvement of the detection and restoration capability.