Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieve better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands through multiscale filtering and extract features to map an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.

Recent CNN-based saliency models have achieved excellent performance on public datasets, but most are sensitive to distortions from noise or compression. In this paper, we propose an end-to-end generic salient object segmentation model called Metric Expression Network (MEnet) to overcome this drawback. We construct a topological metric space where the implicit metric is determined by a deep network. In this latent space, we can group pixels within an observed image semantically into two regions, based on whether they are in a salient region or a non-salient region in the image. We carry out all feature extractions at the pixel level, which makes the output boundaries of the salient object finely-grained. Experimental results show that the proposed metric can generate robust salient maps that allow for object segmentation. By testing the method on several public benchmarks, we show that the performance of MEnet achieves excellent results. We also demonstrate that the proposed method outperforms previous CNN-based methods on distorted images.

We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further improve the de-rained result, we use a priori image domain knowledge by focusing on high frequency detail during training, which removes background interference and focuses the model on the structure of rain in images. This demonstrates that a deep architecture not only has benefits for high-level vision tasks but also can be used to solve low-level imaging problems. Though we train the network on synthetic data, we find that the learned network generalizes well to real-world test images. Experiments show that the proposed method significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures. We discuss applications of this structure to denoising and JPEG artifact reduction at the end of the paper.

We introduce a deep network architecture called DerainNet for removing rain streaks from an image. Based on the deep convolutional neural network (CNN), we directly learn the mapping relationship between rainy and clean image detail layers from data. Because we do not possess the ground truth corresponding to real-world rainy images, we synthesize images with rain for training. In contrast to other common strategies that increase depth or breadth of the network, we use image processing domain knowledge to modify the objective function and improve deraining with a modestly sized CNN. Specifically, we train our DerainNet on the detail (high-pass) layer rather than in the image domain. Though DerainNet is trained on synthetic data, we find that the learned network translates very effectively to real-world images for testing. Moreover, we augment the CNN framework with image enhancement to improve the visual results. Compared with the state-of-the-art single image de-raining methods, our method has improved rain removal and much faster computation time after network training.