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This paper explores learned image compression based on traditional and learned discrete wavelet transform (DWT) architectures and learned entropy models for coding DWT subband coefficients. A learned DWT is obtained through the lifting…

In the next decade, the LSST will become a major facility for the astronomical community. However accurately determining the redshifts of the observed galaxies without using spectroscopy is a major challenge. Reconstruction of the redshifts…

Context. The atmospheric absorption of the Earth is an important limiting factor for ground-based spectroscopic observations and the near-infrared and infrared regions are the most affected. Several software packages that produce a…

In interpretation of remote sensing images, it is possible that some images which are supplied by different sensors become incomprehensible. For better visual perception of these images, it is essential to operate series of pre-processing…

With the development of the convolutional neural network, image style transfer has drawn increasing attention. However, most existing approaches adopt a global feature transformation to transfer style patterns into content images (e.g.,…

Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images. Training these models, however, necessitates large labeled…

Reduction of images and science analysis from ground-based telescopes such as the LSST requires detailed knowledge of the PSF of the image, which includes components attributable to the instrument as well as components attributable to the…

This paper attempts to improve the quality and the modification rate of a Stego Image. The input image provided for estimating the quality of an image and the modified rate is a bitmap image. The threshold value is used as a parameter for…

We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model. One of the key innovations is that the underlying volumetric…

(Abbreviated) We show that the XRT spectral response calibration was complicated by various energy offsets in photon counting (PC) and windowed timing (WT) modes related to the way the CCD is operated in orbit (variation in temperature…

Aims. We present a stand-alone software (named EXSdetect) for the detection of extended sources in X-ray images. Our goal is to provide a flexible tool capable of detecting extended sources down to the lowest flux levels attainable within…

Hyperspectral imaging (HSI) is essential across various disciplines for its capacity to capture rich spectral information. However, efficiently reconstructing hyperspectral images from compressive sensing measurements presents significant…

Photometric redshifts are estimated on the basis of template scenarios with the help of the code ZPEG, an extension of the galaxy evolution model PEGASE.2 and available on the PEGASE web site. The spectral energy distribution (SED)…

Discrete trigonometric transforms (DTTs), such as the DCT-2 and the DST-7, are widely used in video codecs for their balance between coding performance and computational efficiency. In contrast, data-dependent transforms, such as the…

The issue concerning the significant decline in the stability of feature extraction for images subjected to large-angle affine transformations, where the angle exceeds 50 degrees, still awaits a satisfactory solution. Even ASIFT, which is…

Capturing images under extremely low-light conditions poses significant challenges for the standard camera pipeline. Images become too dark and too noisy, which makes traditional enhancement techniques almost impossible to apply. Recently,…

Environmental Sound Classification (ESC) is a rapidly evolving field that recently demonstrated the advantages of application of visual domain techniques to the audio-related tasks. Previous studies indicate that the domain-specific…

In this paper we report on improved part segmentation performance using convolutional neural networks to reduce the dependency on the large amount of manually annotated empirical images. This was achieved by optimising the visual realism of…

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary…

One of the major open problems in computer vision is detection of features in visually impaired images. In this paper, we describe a potential solution using Phase Stretch Transform, a new computational approach for image analysis, edge…