Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Learning Enriched Features for Real Image Restoration and Enhancement.

Abstract: With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at this https URL.

Single image dehazing and denoising with variational method

Abstract: In this paper, we propose an unified variational approach for image dehazing and denoising from a single input image. Total variation regularization terms are used in the energy functional. Also, we use the negative gradient descent method to solve the corresponding Euler-Lagrange equations. To obtain good initial values, we improve the estimation of transmission map with the windows adaptive method based on the dark channel prior which can overcomes the block effects. The numerical results demonstrate that our algorithm is effective and promising.

RGB channel based decision tree grey-alpha medical image steganography with RSA cryptosystem

Abstract: This paper presents the novelty in sensitive data transmission of patient medical records. The secret medical data is hidden inside scanned grey medical image or magnetic resonance image using the red, green, blue, and alpha (RGBA) image and with the help of decision tree. In this technique, alpha channel will be separated from the RGBA image and merged to the medical grey image to improve the hiding capacity. RSA cryptosystem is used to encrypt the medical data, and divided into various blocks using dynamic key. In steganography process, organize the grey-alpha channel medical cover image into various blocks using dynamic key. Secret cipher blocks are assigned to grey-alpha channel medical cover image blocks using Breadth First Search and decision tree, for data embedding. Performance analysis is observed using various performance measure parameters between various medical stego and cover images.

A vision-based hybrid method for facial expression recognition

Abstract: Facial expression is a very useful channel for intelligent human computer communication. In this paper we propose a hybrid method to recognize facial expression. Our main contributions in this study are: first, face region is detected by combing Adaboost, Skin color model and motion history image; second, feature points representing different facial expressions are separated using optical flow; third, a support vector machine classifier is used to classify these feature point's info (location, distance, angle); last, tests to explore the whole facial expression recognition process are conducted and the results are satisfactory.

High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting

Abstract: Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single-size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder-decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three-channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1-score and Overall Accuracy (OA) reached 86.79% and 88.35%, respectively.