- Categories:
- Categories:
- Read more about Gradient Image Super-Resolution for Low-Resolution Image Recognition
- Log in to post comments
In visual object recognition problems essential to surveillance and navigation problems in a variety of military and civilian use cases,low-resolution and low-quality images present great challenges to this problem. Recent advancements in deep learning based methods like EDSR/VDSR have boosted pixel domain image super-resolution(SR) performances significantly in terms of signal to noise ratio(SNR)/mean square error(MSE) metrics of the super-resolved image.
- Categories:
- Read more about CONVOLUTIONAL-SPARSE-CODED DYNAMIC MODE DECOMPOSITION AND ITS APPLICATION TO RIVER STATE ESTIMATION
- Log in to post comments
This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode de-
- Categories:
- Read more about Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks
- Log in to post comments
The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description. The main challenge of LBIE is to disentangle the semantics in image and text and then combine them to generate realistic images. Therefore, the editing performance is heavily dependent on the learned representation.
- Categories:
- Categories:
- Read more about PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING
- Log in to post comments
Guided image filter is one of the most commonly used ways to refine transmission maps. However, since this filter transfers the structures of the guidance image to the filtering output, when the guidance image is the input image itself, even small textures in the input image will cause the change of transmission, which is obviously contrary to the principle that transmission changes only when scene depth changes. In this paper, saliency detection, which simulates the way human eyes work, is introduced into haze removal to tackle the above issue.
- Categories:
- Read more about Image Reflection Removal Using The Wasserstein Generative Adversarial Network
- Log in to post comments
Imaging through a semi-transparent material such as glass often suffers from the reflection problem, which degrades the image quality. Reflection removal is a challenging task since it is severely ill-posed. Traditional methods, while all require long computation time on minimizing different objective functions with huge matrices, do not necessarily give satisfactory performance. In this paper, we propose a novel deep-learning based method to allow fast removal of reflection.
- Categories:
- Read more about MULTI-SCALE SPATIAL-TEMPORAL NETWORK FOR PERSON RE-IDENTIFICATION
- Log in to post comments
last_version1.pdf
- Categories:
Deep convolutional neural networks (CNNs) are nowadays achieving significant leaps in different pattern recognition tasks including action recognition. Current CNNs are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. CNNs also rely on max/average pooling which reduces dimensionality of output layers and hence attenuates their sensitivity to the availability of labeled data.
- Categories: