Deep learning algorithms have demonstrated state-ofthe-art performance in various tasks of image restoration. This was made possible through the ability of CNNs to learn from large exemplar sets. However, the latter becomes an issue for hyperspectral image processing where datasets commonly consist of just a few images. In this work, we propose a new approach to denoising, inpainting, and superresolution of hyperspectral image data using intrinsic properties of a CNN without any training. The performance of the given algorithm is shown to be comparable to the performance of trained networks, while its application is not restricted by the availability of training data. This work is an extension of original "deep prior" algorithm to hyperspectral imaging domain and 3D-convolutional networks.

We investigate methods for the recovery of reflectance spectra from the responses of trichromatic camera systems and the application of these methods to the problem of camera characterization. The recovery of reflectance from colorimetric data is an ill-posed problem, and a unique solution requires additional constraints. We introduce a novel method for reflectance recovery that finds the smoothest spectrum consistent with both the colorimetric data and a linear model of reflectance. Four multispectral methods were tested using data from a real trichromatic camera system. The new method gave the lowest maximum colorimetric error in terms of camera characterization with test data that were independent of the training data. However, the average colorimetric performances of the four multispectral methods were statistically indistinguishable from each other but were significantly worse than conventional methods for camera characterization such as polynomial transforms.

Distance capacities are at the center of vital information investigation and preparing instruments, e.g. PCAarrangement, vector middle channel, and mathematical morphology. Notwithstanding its key part, a separate capacity is frequently utilized without cautious thought of its basic suppositions and scientific development. With the target of recognizing a reasonable separation work for hyper spectral pictures in order to keep up the precision of hyper spectral picture preparing comes about, we look at existing separation capacities and characterize a reasonable arrangement of choice criteria. Remembering that the determination of separation capacities is very identified with the genuine definition of the range, we likewise characterize the current separation capacities in light of how they intrinsically characterize a range. Hypothetical requirements also, conduct, and numerical tests are proposed for the assessment of separation capacities. Concerning the assessment criteria, Euclidean separation of combined range (ECS) was observed to be the most appropriate separation work.

Abstract.A large number of Image Quality (IQ) metrics have been developed over the last decades and the number continues to grow. For development and evaluation of such metrics, IQ databases with reference images, distortions, and perceptual quality data, is very useful. However, existing IQ databases have some drawbacks, making them incapable of evaluating properly all aspects of IQ metrics. The lack of reference image design principles; limited distortion aspects; and uncontrolled viewing conditions. Furthermore, same sets of images are always used for evaluating IQ metrics, so more images are needed. These are some of the reasons why a newly developed IQ database is desired. In this study we propose a new IQ database, Colourlab Image Database: Image Quality (CID:IQ), for which we have proposed methods to design reference images, and different types of distortions have been applied. Another new feature with our database is that we have conducted the perceptual experiments at two viewing distances. The CID:IQ database is available at http://www.colourlab.no/cid.

No abstract