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Recently, the PnP-GAP algorithm has achieved remarkable reconstruction quality for snapshot compressive imaging (SCI), and its convergence has been proven based on the condition of diminishing noise levels and the assumption of

In this paper, a deep learning network with double closed- loop structure is introduced to tackle the image deblurring problem. The first closed-loop in our model is composed of two networks which learn a pair of opposite mappings between the blurry and sharp images. By this way, the solution spaces of possible functions that map a blurry image to its sharp counterpart can be effectively reduced. Furthermore, the first closed-loop also helps our model to deal with the unpaired samples in the training set.

We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similarity path graph (SPG) $\cG$, represented by graph Laplacian matrix $\L$, where the similarities between adjacent frames are encoded as positive edge weights. We show that maximizing the smallest eigenvalue $\lambda_{\min}(\B)$ of a coefficient matrix $\B = \text{diag}(\a) + \mu \L$, where $\a$ is the binary keyframe selection vector, is equivalent to minimizing a worst-case signal reconstruction error.

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved.

The existing low-light image enhancement methods may cause under enhancement, unbalanced brightness and blurriness. To address these shortcomings, we proposed the non-linear mapping method based on the Retinex theory (NMMR). We use an improved traditional gamma function to estimate the reflectance, and we proposed the maximum brightness channel to estimate the illumination.

Video post-processing is a method to improve the quality of reconstructed frames at the
decoder side. Although the existing post-processing algorithms based on deep learning
can achieve signicant quality improvement compared with traditional methods, they will
require a lot of computational resources, which makes these algorithms difficult to use
on mobile devices. To tackle this problem, a low-complexity neural network based on
max-pooling and depth-wise separable convolution is proposed in this work for compressed

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping distortions with the use of content-aware cropping. The pipeline of the proposed approach consists of the following steps. First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models.

Action recognition in top-view 360° videos is an emerging research topic in computer vision. Existing work utilizes a global projection method to transform 360° video frames to panorama frames for further processing. However, this unwrapping suffers from a problem of geometric distortion i.e., people present near the centre in the 360° video frames appear highly stretched and distorted in the corresponding panorama frames (observed in 37.5% of the total panorama frames in 360Action dataset).

High dynamic range (HDR) image formation from low dynamic range (LDR) images of different exposures is a well researched topic in the past two decades.
However, most of the developed techniques consider differently exposed LDR images that are acquired from the same camera view point, which assumes the scene to be static long enough to capture multiple images.
In this paper, we propose to address the problem of HDR imaging from differently exposed LDR stereo images using an encoder-decoder based convolutional neural network (CNN).