Hyperspectral image (HSI) denoising is a critical preprocessing step for ensuring the usability of HSIs. However, current deep learning-based approaches still struggle with modeling the global spectral correlation among bands, which is crucial for high-quality denoising. To address this issue, we introduce a multitask attention module and embed it into a U-Net architecture to yield a multitask attentional U-Net (MTA-Net) for HSI denoising. The module enforces all bands to focus on the same region by sharing the same attention map across all bands. This ensures that all bands capture the same image structure, effectively modeling the global spectral correlation. Experimental results demonstrate that the proposed MTA-Net achieves state-of-the-art performance on both synthetic and real-world data.

With the rapid development of the usage of digital imaging and communication technologies, there appears to be a great demand for fast and practical approaches for image quality assessment (IQA) algorithms that can match human judgements. In this paper, we propose a novel general-purpose no-reference IQA (NR-IQA) framework by means of learning quality-aware filters (QAF). Using these filters for image encoding, we can obtain effective image representations for quality estimation. Additionally, random forest is used to learn the mapping from feature space to human subjective scores. Extensive experiments conducted on LIVE and CSIQ databases demonstrate that the proposed NR-IQA metric QAF can achieve better prediction performance than all the other state-of-the-art NR-IQA approaches in terms of both prediction accuracy and generalization capabilities.

Salient regions detection from images is an important and fundamental research problem in neuroscience and psychology and it serves as an indispensible step for numerous machine vision tasks. In this paper, we propose a novel conceptually simple salient region detection method, namely SDSP, by combining three simple priors. At first, the behavior that the human visual system detects salient objects in a visual scene can be well modeled by band-pass filtering. Secondly, people are more likely to pay their attention on the center of an image. Thirdly, warm colors are more attractive to people than cold colors are. Extensive experiments conducted on the benchmark dataset indicate that SDSP could outperform the other state-of-the-art algorithms by yielding higher saliency prediction accuracy. Moreover, SDSP has a quite low computational complexity, rendering it an outstanding candidate for time critical applications. The Matlab source code of SDSP and the evaluation results have been made online available at http://sse.tongji.edu.cn/linzhang/va/SDSP/SDSP.htm.

The goal of no-reference image quality assessment (NR-IQA) is to estimate the quality of an image consistent with the human perception of the image automatically without any prior of the reference image. In this paper, we present a simple yet efficient and effective approach to learn a blind Image Quality index based on Visual saliency guided sampling and Gabor filtering, namely IQVG. Given an image, we at first randomly sample a sufficient number of image patches guided by the image's visual saliency map and convolve each patch with Gabor filters to get a bag of features. Then, the image is represented by using a histogram to encode the bag of features. Support vector regression (SVR) is used to learn the mapping from feature space to image quality. Extensive experiments conducted on the LIVE IQA database demonstrate the overall superiority of our IQVG over the other state-of-the-art NR-IQA algorithms evaluated. The Matlab source code of IQVG and the evaluation results are available online at http://sse.tongji.edu.cn/linzhang/IQA/IQVG/IQVG.htm.