In this paper we propose image fusion algorithm using hierarchical PCA. Image fusion is a process of combining two or more images (which are registered) of the same scene to get the more informative image. Hierarchical multiscale and multiresolution image processing techniques, pyramid decomposition are the basis for the majority of image fusion algorithms. Principal component analysis (PCA) is a well-known scheme for feature extraction and dimension reduction and is used for image fusion. We propose image fusion algorithm by combining pyramid and PCA techniques and carryout the quality analysis of proposed fusion algorithm without reference image. There is an increasing need for the quality analysis of the fusion algorithms as fusion algorithms are data set dependent. Subjective analysis of fusion algorithm using hierarchical PCA is done by considering the opinion of experts and non experts and for quantitative quality analysis we use different quality metrics. We demonstrate fusion using pyramid, wavelet and PCA fusion techniques and carry out performance analysis for these four fusion methods using different quality measures for variety of data sets and show that proposed image fusion using hierarchical PCA is better for the fusion of multimodal imaged. Visible inspection with quality parameters are used to arrive at a fusion results.

BACKGROUND AND PURPOSE: MR imaging rescans and recalls can create large hospital revenue loss. The purpose of this study was to develop a fast, automated method for assessing rescan need in motion-corrupted brain series. MATERIALS AND METHODS:A deep learning-based approach was developed, outputting a probability for a series to be clinically useful. Comparison of this per-series probability with a threshold, which can depend on scan indication and reading radiologist, determines whether a series needs to be rescanned. The deep learning classification performance was compared with that of 4 technologists and 5 radiologists in 49 test series with low and moderate motion artifacts. These series were assumed to be scanned for 2 scan indications: screening for multiple sclerosis and stroke. RESULTS:The image-quality rating was found to be scan indication-and reading radiologist-dependent. Of the 49 test datasets, technologists created a mean ratio of rescans/recalls of (4.7 Ϯ 5.1)/(9.5 Ϯ 6.8) for MS and (8.6 Ϯ 7.7)/(1.6 Ϯ 1.9) for stroke. With thresholds adapted for scan indication and reading radiologist, deep learning created a rescan/recall ratio of (7.3 Ϯ 2.2)/(3.2 Ϯ 2.5) for MS, and (3.6 Ϯ 1.5)/(2.8 Ϯ 1.6) for stroke. Due to the large variability in the technologists' assessments, it was only the decrease in the recall rate for MS, for which the deep learning algorithm was trained, that was statistically significant (P ϭ .03). CONCLUSIONS:Fast, automated deep learning-based image-quality rating can decrease rescan and recall rates, while rendering them technologist-independent. It was estimated that decreasing rescans and recalls from the technologists' values to the values of deep learning could save hospitals $24,000/scanner/year. ABBREVIATIONS: CB ϭ clinically bad; CG ϭ clinically good; CNN ϭ convolutional neural network; DL ϭ deep learning; D0 -D5 ϭ radiologists; IQ ϭ image quality; R0 ϭ radiologist; ROC ϭ receiver operating characteristic; T1-T4 ϭ MR imaging technologists

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This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://nightimaging.org/

The paper presents activity based teaching learning for undergraduate students. This also gives the analysis of the effectiveness of these activities. The objective of proposed pedagogical practice is to enhance the course learning, beyond the traditional mode. In the traditional mode of teaching the course was not able to make a positive impact on learning. This is because of monotonous lecturing and absence of activities.Activity Based Teaching Learning (ABTL) is an effort to overcome the limitations of traditional mode of course delivery. Progressive pedagogical models are used for the enhancement of course learning. To meet the objective different activities are designed and practiced along with class room teaching. The frame work includes teaching through games in Digital Communication course for 6 th semester Electronics and Communication Engineering (ECE) students and worksheets in Basic Electronics for I st year students. In the proposed approach, the active learning provides more opportunities to learn beyond the classroom teaching.Effectiveness of these activities is assessed through academic performance.

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